knitr::opts_chunk$set(echo = TRUE,eval=TRUE, message=FALSE, warning=FALSE)

Load packages:

library(readxl)
library(magrittr)
library(dplyr)
library(ggplot2)    
library(ggmap)
library(ggthemes)
library(ggpubr)
library(ggforce)
library(tidyverse)
library(gmodels)
library(rgdal)
library(osmdata)
library(nominatim)
library(jsonlite)
library(RColorBrewer)
library(tidyr)
library(leaflet)
library(maps)
library(tigris)
library(tidytext)
library(textdata)
library(tm)
library(quanteda)
library(rvest)
library(stringr)
library(SnowballC)
library(wordcloud)
library(plotrix)
library(qdapDictionaries)
library(formattable)
library(stringr)
library(DT)

Import database:

Tesla<-read.csv("/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/(update)TSLA_sentiment_analysis.csv")

Cleaning the text and word cloud

Text cleaning–Further remove numbers and punctuations

Create and preprocess text in corpus (162 tweets)

doc_id<-c(1:126)
line<-c(rep(1,126))
text<-data.frame(doc_id, text=Tesla$tweet,line, stringsAsFactors=FALSE)
df_source<-DataframeSource(text)
df_corpus<-VCorpus(df_source)
#Clean text
#removefullycap<-function(x){gsub("[A-Z][A-Z]+", " ", x)}
#removeemoji<-function(x){gsub("[^\x01-\x7F]", "", x)}
clean_corpus<-function(corpus){
  #corpus<-tm_map(corpus, content_transformer(removefullycap))
  #corpus<-tm_map(corpus, content_transformer(removeemoji))
  #corpus<-tm_map(corpus, content_transformer(tolower))
  #corpus<-tm_map(corpus, removeWords, c(stopwords("en")))
  corpus<-tm_map(corpus, removeNumbers)
  corpus<-tm_map(corpus, removePunctuation)
  corpus<-tm_map(corpus, stripWhitespace)
  return(corpus)
}
corpus_clean<-clean_corpus(df_corpus)
corpus_dtm<-DocumentTermMatrix(corpus_clean)
corpus_m<-as.matrix(corpus_dtm)
corpus_dtm
<<DocumentTermMatrix (documents: 126, terms: 1572)>>
Non-/sparse entries: 3647/194425
Sparsity           : 98%
Maximal term length: 17
Weighting          : term frequency (tf)
dim(corpus_m)
[1]  126 1572

Word cloud

Calculate tf_itf scores for tweets.

#Tidy objects
corpustd<-tidy(corpus_dtm)
#Calculate frequencies
corpustf_idf<-corpustd %>%
  bind_tf_idf(term, document, count) %>%
  arrange(desc(tf_idf))
corpustf_idf

Word cloud for Tesla CEO’s tweets

purple_orange<-brewer.pal(10, "PuOr")
purple_orange<-purple_orange[-(1:2)]
set.seed(2200)
wordcloud(corpustf_idf$term, corpustf_idf$tf, max.words=100, colors=purple_orange)

Sentiment

(a) Stay positive

Calculate the tone of each text based on the positive and negative words that are being used in the tweets.

First build the sentiment function. Obtain the list of positive.words and negative.words from the sentiment dictionary of Hu & Liu (2004) using the qdapDictionaries package.

sentiment<-function(words=c("really great good stuff bad")){
  tok<-tokens(words)
  pos.count<-sum(tok[[1]] %in% positive.words)
  #cat("\n positive words:",tok[[1]][which(tok[[1]]%in%positive.words)],"\n")
  neg.count<-sum(tok[[1]]%in%negative.words)
  #cat("\n negative words:",tok[[1]][which(tok[[1]]%in%negative.words)],"\n")
  out<-(pos.count-neg.count)/(pos.count+neg.count)
  #cat("\n Tone of Document:",out)
  return(out)
}

Apply the function on text of the 162 tweets.

toneofdocument<-Tesla
toneofdocument<-toneofdocument
for (i in 1:126){
  toneofdocument$toneofdocument[i]<-sentiment(toneofdocument$tweet[i])
}
toneofdocument
write.csv(toneofdocument,"/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Tesla tone of document.csv", row.names = FALSE)

Visualize the relationship between tone of the document and daily stock price change.

plot<-toneofdocument %>%
  ggplot(aes(x=toneofdocument,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Tone of Document", y="Daily stock price change", title="Relationship between Tone of Document and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5, angle=45,size=4.5))+
  coord_flip()
plot

(b) Positive VS Negative

Ignore all documents that do not have words contained within the Hu & Liu dictionary and all neutral scores. Collapse the positive and negative texts into two larger documents.

positivetone<-toneofdocument %>%
  filter(toneofdocument>0) %>%
  select(tweet)
positivetext<-paste(unlist(positivetone$tweet), collapse=" ")
negativetone<-toneofdocument %>%
  filter(toneofdocument<0) %>%
  select(tweet)
negativetext<-paste(unlist(negativetone$tweet), collapse=" ")

Comparison Wordcloud

Generate comparison cloud showing the most-frequent positive and negative words, where document 1 denotes positive texts, and document 2 denotes negative texts.

doc_id<-c("Frequent words in positive sentiment tweets","Frequent words in negative sentiment tweets")
line<-c(1,1)
comparisontext<-data.frame(doc_id, text=c(positivetext, negativetext),line, stringsAsFactors=FALSE)
df_source<-DataframeSource(comparisontext)
df_corpus<-VCorpus(df_source)
corpuscomparison_clean<-clean_corpus(df_corpus)
corpuscomparison_tdm<-TermDocumentMatrix(corpuscomparison_clean)
corpuscomparison_m<-as.matrix(corpuscomparison_tdm)
set.seed(2105)
comparison.cloud(corpuscomparison_m, colors=c("orange", "purple"), scale=c(0.1,2), title.size=1, max.words=100)

(c) Get in their mind

Identify emotions (anger, anticipation, disgust, fear, sadness, sadness, surprise, trust) as well as negative and positive using the NRC Word-Emotion Association Lexicon in the tidytext package.

nrc_anger<-get_sentiments("nrc") %>% 
  filter(sentiment=="anger")
nrc_anticipation<-get_sentiments("nrc") %>% 
  filter(sentiment=="anticipation")
nrc_disgust<-get_sentiments("nrc") %>% 
  filter(sentiment=="disgust")
nrc_fear<-get_sentiments("nrc") %>% 
  filter(sentiment=="fear")
nrc_joy<-get_sentiments("nrc") %>% 
  filter(sentiment=="joy")
nrc_sadness<-get_sentiments("nrc") %>% 
  filter(sentiment=="sadness")
nrc_surprise<-get_sentiments("nrc") %>% 
  filter(sentiment=="surprise")
nrc_trust<-get_sentiments("nrc") %>% 
  filter(sentiment=="trust")
nrc_negative<-get_sentiments("nrc") %>% 
  filter(sentiment=="negative")
nrc_positive<-get_sentiments("nrc") %>% 
  filter(sentiment=="positive")
nrc_anger$word
   [1] "abandoned"        "abandonment"      "abhor"            "abhorrent"        "abolish"          "abomination"     
   [7] "abuse"            "accursed"         "accusation"       "accused"          "accuser"          "accusing"        
  [13] "actionable"       "adder"            "adversary"        "adverse"          "adversity"        "advocacy"        
  [19] "affront"          "aftermath"        "aggravated"       "aggravating"      "aggravation"      "aggression"      
  [25] "aggressive"       "aggressor"        "agitated"         "agitation"        "agony"            "alcoholism"      
  [31] "alienate"         "alienation"       "allegation"       "altercation"      "ambush"           "anarchism"       
  [37] "anarchist"        "anarchy"          "anathema"         "anger"            "angry"            "anguish"         
  [43] "animosity"        "animus"           "annihilate"       "annihilated"      "annihilation"     "annoy"           
  [49] "annoyance"        "annoying"         "antagonism"       "antagonist"       "antagonistic"     "antichrist"      
  [55] "antipathy"        "antisocial"       "antithesis"       "anxiety"          "argue"            "argument"        
  [61] "argumentation"    "arguments"        "armament"         "armed"            "arraignment"      "arrogant"        
  [67] "arson"            "assail"           "assailant"        "assassin"         "assassinate"      "assassination"   
  [73] "assault"          "asshole"          "atrocious"        "atrocity"         "attack"           "attacking"       
  [79] "attorney"         "avarice"          "avenger"          "averse"           "aversion"         "awful"           
  [85] "backbone"         "bad"              "badger"           "badness"          "bane"             "bang"            
  [91] "banger"           "banish"           "banished"         "banishment"       "bankruptcy"       "banshee"         
  [97] "barb"             "barbaric"         "bark"             "barrier"          "bastion"          "battalion"       
 [103] "batter"           "battery"          "battle"           "battled"          "bayonet"          "bear"            
 [109] "bearish"          "beast"            "beating"          "bee"              "belittle"         "belligerent"     
 [115] "bellows"          "belt"             "berserk"          "betray"           "betrayal"         "bias"            
 [121] "bickering"        "bigot"            "bigoted"          "bile"             "birch"            "birthplace"      
 [127] "bitch"            "bitterly"         "bitterness"       "blackmail"        "blame"            "blasphemous"     
 [133] "blasphemy"        "blast"            "blatant"          "blaze"            "blemish"          "blockade"        
 [139] "bloodshed"        "bloodthirsty"     "bloody"           "bogus"            "boisterous"       "bomb"            
 [145] "bombard"          "bombardment"      "bothering"        "bout"             "boxing"           "brawl"           
 [151] "brazen"           "brimstone"        "broil"            "broken"           "brunt"            "brutal"          
 [157] "brutality"        "brute"            "buffet"           "bugaboo"          "bully"            "bummer"          
 [163] "burial"           "burke"            "busted"           "butcher"          "cacophony"        "cad"             
 [169] "callous"          "campaigning"      "cancer"           "cane"             "canker"           "cannon"          
 [175] "carelessness"     "carnage"          "cash"             "casualty"         "catastrophe"      "caution"         
 [181] "celebrity"        "censor"           "chaff"            "challenge"        "chant"            "chaos"           
 [187] "chaotic"          "cheat"            "choke"            "claimant"         "clamor"           "clash"           
 [193] "clashing"         "claw"             "coerce"           "coercion"         "coldness"         "collision"       
 [199] "collusion"        "combat"           "combatant"        "combative"        "commotion"        "communism"       
 [205] "complain"         "complaint"        "complicate"       "compress"         "compulsion"       "concealment"     
 [211] "concussion"       "condemn"          "condemnation"     "condescension"    "confine"          "confined"        
 [217] "confinement"      "confiscate"       "conflagration"    "conflict"         "confront"         "confusion"       
 [223] "conquest"         "conspirator"      "consternation"    "constraint"       "contempt"         "contemptible"    
 [229] "contemptuous"     "contentious"      "contraband"       "contradict"       "controversial"    "convict"         
 [235] "coop"             "copycat"          "corrupting"       "counsellor"       "coup"             "court"           
 [241] "crabby"           "cracked"          "cranky"           "crazed"           "crazy"            "crime"           
 [247] "criminal"         "criminality"      "criticism"        "criticize"        "cross"            "crucifixion"     
 [253] "cruel"            "cruelly"          "cruelty"          "crunch"           "crusade"          "crushed"         
 [259] "crushing"         "cur"              "curse"            "cursed"           "cursing"          "cussed"          
 [265] "cutthroat"        "cutting"          "dabbling"         "daemon"           "damage"           "dame"            
 [271] "damn"             "damnation"        "darkness"         "dashed"           "dastardly"        "deadly"          
 [277] "death"            "deceit"           "deceive"          "deceived"         "decry"            "defamatory"      
 [283] "defect"           "defendant"        "defense"          "defiance"         "defiant"          "deflate"         
 [289] "defraud"          "defy"             "degeneracy"       "delay"            "deleterious"      "delinquent"      
 [295] "delusion"         "delusional"       "demand"           "demolish"         "demon"            "demonic"         
 [301] "denounce"         "denunciation"     "deny"             "deplorable"       "deplore"          "deportation"     
 [307] "depraved"         "depravity"        "depreciate"       "depreciated"      "depressed"        "deprivation"     
 [313] "deranged"         "derision"         "derogation"       "derogatory"       "desecration"      "desert"          
 [319] "deserted"         "deserve"          "desist"           "despair"          "despicable"       "despise"         
 [325] "despotism"        "destroyed"        "destroyer"        "destroying"       "destruction"      "destructive"     
 [331] "detainee"         "deterioration"    "detest"           "detonation"       "detract"          "devastate"       
 [337] "devastating"      "devastation"      "devil"            "diabolical"       "diatribe"         "dictatorial"     
 [343] "dictatorship"     "difficulty"       "disagree"         "disagreeing"      "disagreement"     "disallowed"      
 [349] "disappoint"       "disappointed"     "disapprove"       "disapproved"      "disapproving"     "disaster"        
 [355] "disastrous"       "disclaim"         "discontent"       "discord"          "discriminate"     "discrimination"  
 [361] "disdain"          "disease"          "disfigured"       "disgrace"         "disgraced"        "disgraceful"     
 [367] "disgruntled"      "disgust"          "disgusting"       "dishonest"        "dishonor"         "disillusionment" 
 [373] "disinformation"   "dislike"          "disliked"         "dislocated"       "dismay"           "dismissal"       
 [379] "disobedience"     "disobedient"      "disobey"          "disparage"        "disparaging"      "disparity"       
 [385] "displaced"        "displeased"       "dispossessed"     "dispute"          "disqualified"     "disreputable"    
 [391] "disrespect"       "disrespectful"    "disruption"       "dissension"       "disservice"       "dissident"       
 [397] "dissolution"      "dissonance"       "distracted"       "distracting"      "distress"         "distressing"     
 [403] "distrust"         "disturbance"      "disturbed"        "disused"          "divorce"          "dominate"        
 [409] "domination"       "doomsday"         "dreadful"         "duel"             "dumps"            "dupe"            
 [415] "duplicity"        "duress"           "dying"            "earthquake"       "effigy"           "egregious"       
 [421] "elbow"            "elf"              "elimination"      "encumbrance"      "endless"          "enemy"           
 [427] "enforce"          "enmity"           "enslaved"         "entangled"        "epidemic"         "eradicate"       
 [433] "eradication"      "erupt"            "eruption"         "escalate"         "eschew"           "evade"           
 [439] "eviction"         "evil"             "exacerbation"     "exaggerate"       "exasperation"     "excitation"      
 [445] "excite"           "execution"        "executioner"      "exile"            "expel"            "expletive"       
 [451] "explode"          "explosive"        "expulsion"        "extermination"    "extinguish"       "failing"         
 [457] "fallacious"       "falsehood"        "falsification"    "fatal"            "fear"             "fee"             
 [463] "feeling"          "fenced"           "ferocious"        "ferocity"         "fervor"           "feud"            
 [469] "feudalism"        "fib"              "fiend"            "fierce"           "fight"            "fighting"        
 [475] "firearms"         "fits"             "flagrant"         "fleece"           "flog"             "fluctuation"     
 [481] "foe"              "foray"            "forbidding"       "force"            "forcibly"         "forearm"         
 [487] "forfeit"          "forsaken"         "foul"             "fraud"            "fraudulent"       "frenetic"        
 [493] "frenzied"         "friction"         "frightful"        "frowning"         "frustrate"        "frustrated"      
 [499] "frustration"      "fugitive"         "fuming"           "furious"          "furiously"        "furnace"         
 [505] "furor"            "fury"             "fuss"             "gall"             "gallows"          "gang"            
 [511] "gent"             "gibberish"        "glare"            "glaring"          "gnome"            "godless"         
 [517] "gonorrhea"        "gore"             "gory"             "grab"             "grated"           "grating"         
 [523] "greed"            "grievance"        "grievous"         "grim"             "grope"            "growl"           
 [529] "growling"         "grudge"           "gruff"            "grumble"          "grumpy"           "guillotine"      
 [535] "guilty"           "gun"              "halter"           "hamstring"        "hanging"          "harass"          
 [541] "harassing"        "harbinger"        "hardened"         "harmful"          "harry"            "harshness"       
 [547] "hate"             "hateful"          "hating"           "hatred"           "haughty"          "havoc"           
 [553] "hell"             "hellish"          "hiss"             "hit"              "hoax"             "holocaust"       
 [559] "homeless"         "homicidal"        "homicide"         "honest"           "hood"             "hoot"            
 [565] "hopelessness"     "horrible"         "horrid"           "horrific"         "horror"           "hostage"         
 [571] "hostile"          "hostilities"      "hostility"        "hot"              "howl"             "huff"            
 [577] "humbug"           "humiliate"        "hunting"          "hurt"             "hurtful"          "hurting"         
 [583] "hysterical"       "idiocy"           "idiotic"          "ill"              "illegal"          "illegality"      
 [589] "illegitimate"     "illicit"          "immaturity"       "immoral"          "immorality"       "impermeable"     
 [595] "implicate"        "impotence"        "imprisoned"       "imprisonment"     "inadmissible"     "inappropriate"   
 [601] "inattention"      "incarceration"    "incase"           "incendiary"       "incense"          "incest"          
 [607] "incite"           "incompatible"     "incompetent"      "incongruous"      "inconsiderate"    "inconvenient"    
 [613] "incredulous"      "incurable"        "indecency"        "indenture"        "indict"           "indifference"    
 [619] "indignant"        "indignation"      "indoctrination"   "inept"            "inequality"       "inexcusable"     
 [625] "infamous"         "infanticide"      "infantile"        "inferno"          "infidel"          "infidelity"      
 [631] "inflict"          "infraction"       "inhibit"          "inhuman"          "inimical"         "injure"          
 [637] "injurious"        "injury"           "injustice"        "inoperative"      "insane"           "insanity"        
 [643] "insecure"         "insidious"        "insignificant"    "instinctive"      "insufficiency"    "insult"          
 [649] "insulting"        "insurrection"     "intense"          "interminable"     "interrupt"        "intimidation"    
 [655] "intolerable"      "intolerance"      "intolerant"       "intractable"      "intruder"         "intrusive"       
 [661] "invade"           "invader"          "invasion"         "involution"       "involvement"      "irate"           
 [667] "ire"              "irreconcilable"   "irritability"     "irritable"        "irritating"       "irritation"      
 [673] "jab"              "jealous"          "jealousy"         "jeopardize"       "jerk"             "kick"            
 [679] "kicking"          "kidnap"           "killing"          "lace"             "lagging"          "lash"            
 [685] "latent"           "lava"             "lawlessness"      "lawsuit"          "lawyer"           "legalized"       
 [691] "leukemia"         "libel"            "liberate"         "lie"              "lightning"        "limited"         
 [697] "liquor"           "litigate"         "litigious"        "livid"            "loath"            "loathe"          
 [703] "loathsome"        "lonely"           "lose"             "losing"           "loss"             "loudness"        
 [709] "lunacy"           "lunatic"          "lying"            "lynch"            "mad"              "madden"          
 [715] "madman"           "madness"          "malevolent"       "malice"           "malicious"        "malign"          
 [721] "malignant"        "malpractice"      "mangle"           "maniac"           "manipulation"     "manslaughter"    
 [727] "martial"          "masochism"        "massacre"         "mastery"          "meddle"           "melodrama"       
 [733] "menace"           "menacing"         "mighty"           "militia"          "misbehavior"      "misconception"   
 [739] "miserable"        "misery"           "mislead"          "misleading"       "misplace"         "misrepresented"  
 [745] "misstatement"     "mistress"         "misunderstanding" "mob"              "mocking"          "molestation"     
 [751] "money"            "monstrosity"      "moody"            "moral"            "morals"           "morbidity"       
 [757] "mortality"        "mosque"           "mosquito"         "mournful"         "muff"             "mug"             
 [763] "mule"             "murder"           "murderer"         "murderous"        "musical"          "mutilation"      
 [769] "mutiny"           "mutter"           "myopia"           "nag"              "nasty"            "negation"        
 [775] "neglected"        "nepotism"         "nether"           "nettle"           "noisy"            "noncompliance"   
 [781] "notoriety"        "nuisance"         "nurture"          "objection"        "obliging"         "obliterate"      
 [787] "obliterated"      "oblivion"         "obnoxious"        "obscenity"        "obstacle"         "obstruct"        
 [793] "obstructive"      "odious"           "offend"           "offended"         "offender"         "offense"         
 [799] "offensive"        "onerous"          "opera"            "opinionated"      "opium"            "opponent"        
 [805] "opposed"          "opposition"       "oppress"          "oppression"       "oppressive"       "oppressor"       
 [811] "orc"              "orchestra"        "ordeal"           "oust"             "outburst"         "outcry"          
 [817] "outrage"          "overbearing"      "overpowering"     "overpriced"       "owing"            "painful"         
 [823] "paralysis"        "paralyzed"        "pare"             "patter"           "paucity"          "payback"         
 [829] "penalty"          "penetration"      "penitentiary"     "perdition"        "pernicious"       "perpetrator"     
 [835] "persecute"        "persecution"      "perverse"         "perversion"       "pervert"          "pessimism"       
 [841] "pest"             "phony"            "picket"           "picketing"        "pillage"          "pique"           
 [847] "pirate"           "pitfall"          "playful"          "plunder"          "poaching"         "poison"          
 [853] "poisoned"         "poisonous"        "polemic"          "politics"         "possessed"        "possession"      
 [859] "pound"            "poverty"          "pow"              "powerful"         "powerless"        "preclude"        
 [865] "prejudice"        "prejudicial"      "presumptuous"     "pretending"       "prick"            "prison"          
 [871] "prisoner"         "profane"          "profanity"        "prohibited"       "prosecute"        "provocation"     
 [877] "provoking"        "pry"              "psychosis"        "punch"            "punished"         "punishing"       
 [883] "punishment"       "punitive"         "quandary"         "quarrel"          "rabble"           "rabid"           
 [889] "rage"             "raging"           "raid"             "rail"             "ram"              "rampage"         
 [895] "ransom"           "rape"             "rapping"          "rascal"           "rating"           "rave"            
 [901] "ravenous"         "raving"           "react"            "rebel"            "rebellion"        "recalcitrant"    
 [907] "recession"        "recidivism"       "reckless"         "recklessness"     "reject"           "rejection"       
 [913] "rejects"          "remand"           "remiss"           "remove"           "renegade"         "renounce"        
 [919] "repay"            "repellent"        "reprimand"        "reprisal"         "reproach"         "repudiation"     
 [925] "resent"           "resentful"        "resentment"       "resign"           "resistance"       "resisting"       
 [931] "restitution"      "restrain"         "restriction"      "retaliate"        "retaliation"      "retaliatory"     
 [937] "retract"          "retribution"      "revenge"          "reversal"         "revoke"           "revolt"          
 [943] "revolting"        "revolution"       "revolver"         "revulsion"        "rheumatism"       "ribbon"          
 [949] "ridicule"         "ridiculous"       "rifle"            "ringer"           "riot"             "riotous"         
 [955] "rivalry"          "rob"              "robbery"          "rocket"           "rook"             "row"             
 [961] "ruined"           "ruinous"          "ruthless"         "saber"            "sabotage"         "saloon"          
 [967] "sarcasm"          "satanic"          "savage"           "savagery"         "scandalous"       "scapegoat"       
 [973] "scar"             "scarcity"         "scare"            "schism"           "schizophrenia"    "scoff"           
 [979] "scold"            "scolding"         "scorching"        "scorn"            "scorpion"         "scoundrel"       
 [985] "scourge"          "scrapie"          "scream"           "screaming"        "screwed"          "sectarian"       
 [991] "sedition"         "segregate"        "selfish"          "senseless"        "sentence"         "separatist"      
 [997] "shackle"          "shaky"            "sham"             "sharpen"         
 [ reached getOption("max.print") -- omitted 247 entries ]
angryf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  angercount<-sum(tok[[1]] %in% nrc_anger$word)
  angerf<-angercount/wordcount
  return(angerf)
}
anticipationf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  anticipationcount<-sum(tok[[1]] %in% nrc_anticipation$word)
  anticipationf<-anticipationcount/wordcount
  return(anticipationf)
}  
disgustf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  disgustcount<-sum(tok[[1]] %in% nrc_disgust$word)
  disgustf<-disgustcount/wordcount
  return(disgustf)
}
fearf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  fearcount<-sum(tok[[1]] %in% nrc_fear$word)
  fearf<-fearcount/wordcount
  return(fearf)
}
joyf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  joycount<-sum(tok[[1]] %in% nrc_joy$word)
  joyf<-joycount/wordcount
  return(joyf)
}
sadnessf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  sadnesscount<-sum(tok[[1]] %in% nrc_surprise$word)
  sadnessf<-sadnesscount/wordcount
  return(sadnessf)
}
surprisef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  surprisecount<-sum(tok[[1]] %in% nrc_sadness$word)
  surprisef<-surprisecount/wordcount
  return(surprisef)
}
trustf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  trustcount<-sum(tok[[1]] %in% nrc_trust$word)
  trustf<-trustcount/wordcount
  return(trustf)
}
negativef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  negativecount<-sum(tok[[1]] %in% nrc_negative$word)
  negativef<-negativecount/wordcount
  return(negativef)
}
positivef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  positivecount<-sum(tok[[1]] %in% nrc_positive$word)
  positivef<-positivecount/wordcount
  return(positivef)
}
emotionsfrequency<-Tesla
for (i in 1:126){
  emotionsfrequency$angryf[i]<-angryf(emotionsfrequency$tweet[i])
  emotionsfrequency$anticipationf[i]<-anticipationf(emotionsfrequency$tweet[i])
  emotionsfrequency$disgustf[i]<-disgustf(emotionsfrequency$tweet[i])
  emotionsfrequency$fearf[i]<-fearf(emotionsfrequency$tweet[i])
  emotionsfrequency$joyf[i]<-joyf(emotionsfrequency$tweet[i])
  emotionsfrequency$sadnessf[i]<-sadnessf(emotionsfrequency$tweet[i])
  emotionsfrequency$surprisef[i]<-surprisef(emotionsfrequency$tweet[i])
  emotionsfrequency$trustf[i]<-trustf(emotionsfrequency$tweet[i])
  emotionsfrequency$negativef[i]<-negativef(emotionsfrequency$tweet[i])
  emotionsfrequency$positivef[i]<-positivef(emotionsfrequency$tweet[i])
  
  emotionsfrequency$langryf[i]<-log(emotionsfrequency$angryf[i]+1)
  emotionsfrequency$lanticipationf[i]<-log(emotionsfrequency$anticipationf[i]+1)
  emotionsfrequency$ldisgustf[i]<-log(emotionsfrequency$disgustf[i]+1)
  emotionsfrequency$lfearf[i]<-log(emotionsfrequency$fearf[i]+1)
  emotionsfrequency$ljoyf[i]<-log(emotionsfrequency$joyf[i]+1)
  emotionsfrequency$lsadnessf[i]<-log(emotionsfrequency$sadnessf[i]+1)
  emotionsfrequency$lsurprisef[i]<-log(emotionsfrequency$surprisef[i]+1)
  emotionsfrequency$ltrustf[i]<-log(emotionsfrequency$trustf[i]+1)
  emotionsfrequency$lnegativef[i]<-log(emotionsfrequency$negativef[i]+1)
  emotionsfrequency$lpositivef[i]<-log(emotionsfrequency$positivef[i]+1)
}
emotionsfrequency
write.csv(emotionsfrequency,"/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Tesla frequency of emotions.csv", row.names = FALSE)
plotangry<-emotionsfrequency %>%
  ggplot(aes(x=langryf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of angry words in the text (Log)", y="Daily stock price change", title="Relationship between Angry emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotangry

#ggsave('Angry-linear.png')
plotanticipation<-emotionsfrequency %>%
  ggplot(aes(x=lanticipationf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of anticipation words in the text (Log)", y="Daily stock price change", title="Relationship between Anticipation emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotanticipation

#ggsave('Anticipation-linear.png')
plotdisgust<-emotionsfrequency %>%
  ggplot(aes(x=ldisgustf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of disgust words in the text (Log)", y="Daily stock price change", title="Relationship between Disgust emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotdisgust

#ggsave('Disgust-linear.png')
plotjoy<-emotionsfrequency %>%
  ggplot(aes(x=ljoyf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of joy words in the text (Log)", y="Daily stock price change", title="Relationship between Joy emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotjoy

#ggsave('Joy-linear.png')
plotfear<-emotionsfrequency %>%
  ggplot(aes(x=lfearf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of fear words in the text (Log)", y="Daily stock price change", title="Relationship between Fear emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotfear

#ggsave('Fear-linear.png')
plotsadness<-emotionsfrequency %>%
  ggplot(aes(x=lsadnessf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of sadness words in the text (Log)", y="Daily stock price change", title="Relationship between Sadness emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotsadness

#ggsave('Sadness-linear.png')
plotsurprise<-emotionsfrequency %>%
  ggplot(aes(x=lsurprisef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of sadness words in the text (Log)", y="Daily stock price change", title="Relationship between Surprise emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotsurprise

#ggsave('Surprise-linear.png')
plottrust<-emotionsfrequency %>%
  ggplot(aes(x=ltrustf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of trust words in the text (Log)", y="Daily stock price change", title="Relationship between Trust emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plottrust

#ggsave('Trust-linear.png')
plotnegative<-emotionsfrequency %>%
  ggplot(aes(x=lnegativef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of negative words in the text (Log)", y="Daily stock price change", title="Relationship between Negative emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotnegative

#ggsave('Negative-linear.png')
plotpositive<-emotionsfrequency %>%
  ggplot(aes(x=lpositivef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of positive words in the text (Log)", y="Daily stock price change", title="Relationship between Positive emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotpositive

#ggsave('Positive-linear.png')

Linear regression

emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
lm<-lm(DailyChange ~ angryf+anticipationf+disgustf+fearf+joyf+sadnessf+surprisef+trustf+negativef+positivef, data=emotionsfrequency)
summary(lm)

Call:
lm(formula = DailyChange ~ angryf + anticipationf + disgustf + 
    fearf + joyf + sadnessf + surprisef + trustf + negativef + 
    positivef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.071299 -0.005739 -0.000184  0.005641  0.069719 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)  
(Intercept)    0.001005   0.003243   0.310   0.7573  
angryf        -0.010694   0.033506  -0.319   0.7502  
anticipationf  0.055052   0.034698   1.587   0.1154  
disgustf      -0.118751   0.053738  -2.210   0.0292 *
fearf          0.003373   0.040620   0.083   0.9340  
joyf          -0.048200   0.043275  -1.114   0.2678  
sadnessf       0.009555   0.056688   0.169   0.8665  
surprisef      0.057796   0.049961   1.157   0.2498  
trustf        -0.024241   0.025739  -0.942   0.3483  
negativef      0.012257   0.033866   0.362   0.7181  
positivef      0.003699   0.024363   0.152   0.8796  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01639 on 111 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.08138,   Adjusted R-squared:  -0.001374 
F-statistic: 0.9834 on 10 and 111 DF,  p-value: 0.4621
llm<-lm(DailyChange ~ langryf+lanticipationf+ldisgustf+lfearf+ljoyf+lsadnessf+lsurprisef+ltrustf+lnegativef+lpositivef, data=emotionsfrequency)
summary(llm)

Call:
lm(formula = DailyChange ~ langryf + lanticipationf + ldisgustf + 
    lfearf + ljoyf + lsadnessf + lsurprisef + ltrustf + lnegativef + 
    lpositivef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.070962 -0.005960 -0.000026  0.005853  0.069116 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)  
(Intercept)     0.0008243  0.0033894   0.243   0.8083  
langryf        -0.0133337  0.0370231  -0.360   0.7194  
lanticipationf  0.0616512  0.0375109   1.644   0.1031  
ldisgustf      -0.1303422  0.0597663  -2.181   0.0313 *
lfearf          0.0054824  0.0443386   0.124   0.9018  
ljoyf          -0.0478190  0.0471451  -1.014   0.3126  
lsadnessf       0.0081724  0.0590289   0.138   0.8901  
lsurprisef      0.0593145  0.0542168   1.094   0.2763  
ltrustf        -0.0227729  0.0293115  -0.777   0.4389  
lnegativef      0.0142355  0.0372961   0.382   0.7034  
lpositivef      0.0012925  0.0277854   0.047   0.9630  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.01641 on 111 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.07963,   Adjusted R-squared:  -0.003291 
F-statistic: 0.9603 on 10 and 111 DF,  p-value: 0.482
emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
lmangry<-lm(DailyChange ~ angryf, data=emotionsfrequency)
summary(lmangry)

Call:
lm(formula = DailyChange ~ angryf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075182 -0.005894  0.000326  0.005573  0.078318 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.001882   0.001647   1.143    0.255
angryf      -0.001529   0.025035  -0.061    0.951

Residual standard error: 0.01645 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  3.107e-05, Adjusted R-squared:  -0.008302 
F-statistic: 0.003728 on 1 and 120 DF,  p-value: 0.9514
emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
llmangry<-lm(DailyChange ~ langryf, data=emotionsfrequency)
summary(llmangry)

Call:
lm(formula = DailyChange ~ langryf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075244 -0.005944  0.000353  0.005706  0.078256 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.001944   0.001672   1.163    0.247
langryf     -0.003967   0.028906  -0.137    0.891

Residual standard error: 0.01645 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0001569, Adjusted R-squared:  -0.008175 
F-statistic: 0.01884 on 1 and 120 DF,  p-value: 0.8911
lmanticipation<-lm(DailyChange ~ anticipationf, data=emotionsfrequency)
summary(lmanticipation)

Call:
lm(formula = DailyChange ~ anticipationf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.073232 -0.005955  0.000723  0.005658  0.077430 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)
(Intercept)   -6.754e-05  1.995e-03  -0.034    0.973
anticipationf  3.122e-02  2.197e-02   1.421    0.158

Residual standard error: 0.01631 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.01655,   Adjusted R-squared:  0.008353 
F-statistic: 2.019 on 1 and 120 DF,  p-value: 0.1579
llmanticipation<-lm(DailyChange ~ lanticipationf, data=emotionsfrequency)
summary(llmanticipation)

Call:
lm(formula = DailyChange ~ lanticipationf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.072841 -0.005816  0.000733  0.005788  0.077179 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)
(Intercept)    -0.0004587  0.0020562  -0.223    0.824
lanticipationf  0.0399860  0.0249577   1.602    0.112

Residual standard error: 0.01627 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.02094,   Adjusted R-squared:  0.01278 
F-statistic: 2.567 on 1 and 120 DF,  p-value: 0.1118
lmdisgust<-lm(DailyChange ~ disgustf, data=emotionsfrequency)
summary(lmdisgust)

Call:
lm(formula = DailyChange ~ disgustf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074364 -0.005555  0.000350  0.005625  0.077750 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002450   0.001518   1.614    0.109
disgustf    -0.048521   0.029303  -1.656    0.100

Residual standard error: 0.01626 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.02234,   Adjusted R-squared:  0.01419 
F-statistic: 2.742 on 1 and 120 DF,  p-value: 0.1004
llmdisgust<-lm(DailyChange ~ ldisgustf, data=emotionsfrequency)
summary(llmdisgust)

Call:
lm(formula = DailyChange ~ ldisgustf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074201 -0.005650  0.000312  0.005587  0.077712 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002488   0.001527   1.629    0.106
ldisgustf   -0.056354   0.034959  -1.612    0.110

Residual standard error: 0.01627 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0212,    Adjusted R-squared:  0.01304 
F-statistic: 2.599 on 1 and 120 DF,  p-value: 0.1096
lmfear<-lm(DailyChange ~ fearf, data=emotionsfrequency)
summary(lmfear)

Call:
lm(formula = DailyChange ~ fearf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075584 -0.005806  0.000325  0.005954  0.079016 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002284   0.001723   1.326    0.187
fearf       -0.012097   0.023666  -0.511    0.610

Residual standard error: 0.01643 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002173,  Adjusted R-squared:  -0.006143 
F-statistic: 0.2613 on 1 and 120 DF,  p-value: 0.6102
llmfear<-lm(DailyChange ~ lfearf, data=emotionsfrequency)
summary(llmfear)

Call:
lm(formula = DailyChange ~ lfearf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075475 -0.005804  0.000312  0.005948  0.078872 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002175   0.001758   1.237    0.219
lfearf      -0.009741   0.027172  -0.358    0.721

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.00107,   Adjusted R-squared:  -0.007255 
F-statistic: 0.1285 on 1 and 120 DF,  p-value: 0.7206
lmjoy<-lm(DailyChange ~ joyf, data=emotionsfrequency)
summary(lmjoy)

Call:
lm(formula = DailyChange ~ joyf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075065 -0.005687  0.000326  0.005356  0.078557 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001643   0.001823   0.901    0.369
joyf        0.004248   0.022790   0.186    0.852

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0002894, Adjusted R-squared:  -0.008042 
F-statistic: 0.03474 on 1 and 120 DF,  p-value: 0.8525
llmjoy<-lm(DailyChange ~ ljoyf, data=emotionsfrequency)
summary(llmjoy)

Call:
lm(formula = DailyChange ~ ljoyf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075023 -0.005829  0.000259  0.005307  0.078693 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001507   0.001872   0.805    0.422
ljoyf       0.007659   0.026146   0.293    0.770

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0007146, Adjusted R-squared:  -0.007613 
F-statistic: 0.08581 on 1 and 120 DF,  p-value: 0.7701
lmsadness<-lm(DailyChange ~ sadnessf, data=emotionsfrequency)
summary(lmsadness)

Call:
lm(formula = DailyChange ~ sadnessf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074977 -0.005841  0.000223  0.005648  0.078523 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001677   0.001767   0.949    0.345
sadnessf    0.007122   0.041773   0.170    0.865

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0002422, Adjusted R-squared:  -0.008089 
F-statistic: 0.02907 on 1 and 120 DF,  p-value: 0.8649
llmsadness<-lm(DailyChange ~ lsadnessf, data=emotionsfrequency)
summary(llmsadness)

Call:
lm(formula = DailyChange ~ lsadnessf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074940 -0.005820  0.000260  0.005638  0.078560 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001640   0.001783   0.920    0.359
lsadnessf   0.009051   0.044612   0.203    0.840

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0003429, Adjusted R-squared:  -0.007988 
F-statistic: 0.04116 on 1 and 120 DF,  p-value: 0.8396
lmsurprise<-lm(DailyChange ~ surprisef, data=emotionsfrequency)
summary(lmsurprise)

Call:
lm(formula = DailyChange ~ surprisef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075154 -0.005886  0.000335  0.005630  0.078597 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.001970   0.001672   1.178    0.241
surprisef   -0.004029   0.023518  -0.171    0.864

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0002445, Adjusted R-squared:  -0.008087 
F-statistic: 0.02935 on 1 and 120 DF,  p-value: 0.8643
llmsurprise<-lm(DailyChange ~ lsurprisef, data=emotionsfrequency)
summary(llmsurprise)

Call:
lm(formula = DailyChange ~ lsurprisef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075138 -0.005857  0.000290  0.005470  0.078335 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0018256  0.0016966   1.076    0.284
lsurprisef  0.0004571  0.0269478   0.017    0.986

Residual standard error: 0.01645 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  2.397e-06, Adjusted R-squared:  -0.008331 
F-statistic: 0.0002877 on 1 and 120 DF,  p-value: 0.9865
lmtrust<-lm(DailyChange ~ trustf, data=emotionsfrequency)
summary(lmtrust)

Call:
lm(formula = DailyChange ~ trustf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075525 -0.006136  0.000372  0.005620  0.078448 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002441   0.002261   1.080    0.282
trustf      -0.007579   0.021436  -0.354    0.724

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.001041,  Adjusted R-squared:  -0.007284 
F-statistic: 0.125 on 1 and 120 DF,  p-value: 0.7243
llmtrust<-lm(DailyChange ~ ltrustf, data=emotionsfrequency)
summary(llmtrust)

Call:
lm(formula = DailyChange ~ ltrustf, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075296 -0.006046  0.000305  0.005511  0.078403 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.002091   0.002341   0.893    0.373
ltrustf     -0.003384   0.024251  -0.140    0.889

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0001622, Adjusted R-squared:  -0.00817 
F-statistic: 0.01947 on 1 and 120 DF,  p-value: 0.8893
lmnegative<-lm(DailyChange ~ negativef, data=emotionsfrequency)
summary(lmnegative)

Call:
lm(formula = DailyChange ~ negativef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075138 -0.005861  0.000277  0.005421  0.078337 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0017945  0.0018334   0.979    0.330
negativef   0.0007527  0.0179747   0.042    0.967

Residual standard error: 0.01645 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  1.461e-05, Adjusted R-squared:  -0.008319 
F-statistic: 0.001753 on 1 and 120 DF,  p-value: 0.9667
llmnegative<-lm(DailyChange ~ lnegativef, data=emotionsfrequency)
summary(llmnegative)

Call:
lm(formula = DailyChange ~ lnegativef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.075141 -0.005859  0.000190  0.005298  0.078262 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001669   0.001881   0.887    0.377
lnegativef  0.003095   0.020845   0.148    0.882

Residual standard error: 0.01644 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.0001837, Adjusted R-squared:  -0.008148 
F-statistic: 0.02205 on 1 and 120 DF,  p-value: 0.8822
lmpositive<-lm(DailyChange ~ positivef, data=emotionsfrequency)
summary(lmpositive)

Call:
lm(formula = DailyChange ~ positivef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074392 -0.005560  0.000299  0.005273  0.079320 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.000880   0.002621   0.336    0.738
positivef   0.007419   0.016683   0.445    0.657

Residual standard error: 0.01643 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.001645,  Adjusted R-squared:  -0.006675 
F-statistic: 0.1977 on 1 and 120 DF,  p-value: 0.6574
llmpositive<-lm(DailyChange ~ lpositivef, data=emotionsfrequency)
summary(llmpositive)

Call:
lm(formula = DailyChange ~ lpositivef, data = emotionsfrequency)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.074230 -0.005559  0.000262  0.005377  0.079553 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0006468  0.0027280   0.237    0.813
lpositivef  0.0100556  0.0192826   0.521    0.603

Residual standard error: 0.01643 on 120 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.002261,  Adjusted R-squared:  -0.006053 
F-statistic: 0.2719 on 1 and 120 DF,  p-value: 0.603
library(stargazer)
stargazer(llm, type="text", title="Emotion Analysis: Multilinear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multilinear Regression Result (Log).txt")

Emotion Analysis: Multilinear Regression Result
===============================================
                        Dependent variable:    
                    ---------------------------
                            DailyChange        
-----------------------------------------------
langryf                       -0.013           
                              (0.037)          
                                               
lanticipationf                 0.062           
                              (0.038)          
                                               
ldisgustf                    -0.130**          
                              (0.060)          
                                               
lfearf                         0.005           
                              (0.044)          
                                               
ljoyf                         -0.048           
                              (0.047)          
                                               
lsadnessf                      0.008           
                              (0.059)          
                                               
lsurprisef                     0.059           
                              (0.054)          
                                               
ltrustf                       -0.023           
                              (0.029)          
                                               
lnegativef                     0.014           
                              (0.037)          
                                               
lpositivef                     0.001           
                              (0.028)          
                                               
Constant                       0.001           
                              (0.003)          
                                               
-----------------------------------------------
Observations                    122            
R2                             0.080           
Adjusted R2                   -0.003           
Residual Std. Error      0.016 (df = 111)      
F Statistic            0.960 (df = 10; 111)    
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
stargazer(llm, type="text", title="Emotion Analysis: Multilinear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multilinear Regression Result (Log).pdf")

Emotion Analysis: Multilinear Regression Result
===============================================
                        Dependent variable:    
                    ---------------------------
                            DailyChange        
-----------------------------------------------
langryf                       -0.013           
                              (0.037)          
                                               
lanticipationf                 0.062           
                              (0.038)          
                                               
ldisgustf                    -0.130**          
                              (0.060)          
                                               
lfearf                         0.005           
                              (0.044)          
                                               
ljoyf                         -0.048           
                              (0.047)          
                                               
lsadnessf                      0.008           
                              (0.059)          
                                               
lsurprisef                     0.059           
                              (0.054)          
                                               
ltrustf                       -0.023           
                              (0.029)          
                                               
lnegativef                     0.014           
                              (0.037)          
                                               
lpositivef                     0.001           
                              (0.028)          
                                               
Constant                       0.001           
                              (0.003)          
                                               
-----------------------------------------------
Observations                    122            
R2                             0.080           
Adjusted R2                   -0.003           
Residual Std. Error      0.016 (df = 111)      
F Statistic            0.960 (df = 10; 111)    
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
library(stargazer)
stargazer(lm, type="text", title="Emotion Analysis: Multilinear Regression Result", digits=1, out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis:Multilinear Regression Result.txt")

Emotion Analysis: Multilinear Regression Result
===============================================
                        Dependent variable:    
                    ---------------------------
                            DailyChange        
-----------------------------------------------
angryf                         -0.01           
                              (0.03)           
                                               
anticipationf                   0.1            
                              (0.03)           
                                               
disgustf                      -0.1**           
                               (0.1)           
                                               
fearf                          0.003           
                              (0.04)           
                                               
joyf                           -0.05           
                              (0.04)           
                                               
sadnessf                       0.01            
                               (0.1)           
                                               
surprisef                       0.1            
                              (0.05)           
                                               
trustf                         -0.02           
                              (0.03)           
                                               
negativef                      0.01            
                              (0.03)           
                                               
positivef                      0.004           
                              (0.02)           
                                               
Constant                       0.001           
                              (0.003)          
                                               
-----------------------------------------------
Observations                    122            
R2                              0.1            
Adjusted R2                   -0.001           
Residual Std. Error       0.02 (df = 111)      
F Statistic             1.0 (df = 10; 111)     
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
stargazer(llmangry, llmanticipation, llmdisgust, llmfear, llmjoy, llmsadness, llmsurprise, llmtrust, llmpositive, llmnegative,  type="text", title="Emotion Analysis:Linear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Linear Regression Result (Log).txt")

Emotion Analysis:Linear Regression Result
==============================================================================================================
                                                             Dependent variable:                              
                               -------------------------------------------------------------------------------
                                                                 DailyChange                                  
                                 (1)     (2)     (3)     (4)     (5)     (6)     (7)     (8)     (9)    (10)  
--------------------------------------------------------------------------------------------------------------
langryf                        -0.004                                                                         
                               (0.029)                                                                        
                                                                                                              
lanticipationf                          0.040                                                                 
                                       (0.025)                                                                
                                                                                                              
ldisgustf                                      -0.056                                                         
                                               (0.035)                                                        
                                                                                                              
lfearf                                                 -0.010                                                 
                                                       (0.027)                                                
                                                                                                              
ljoyf                                                           0.008                                         
                                                               (0.026)                                        
                                                                                                              
lsadnessf                                                               0.009                                 
                                                                       (0.045)                                
                                                                                                              
lsurprisef                                                                     0.0005                         
                                                                               (0.027)                        
                                                                                                              
ltrustf                                                                                -0.003                 
                                                                                       (0.024)                
                                                                                                              
lpositivef                                                                                      0.010         
                                                                                               (0.019)        
                                                                                                              
lnegativef                                                                                              0.003 
                                                                                                       (0.021)
                                                                                                              
Constant                        0.002  -0.0005  0.002   0.002   0.002   0.002   0.002   0.002   0.001   0.002 
                               (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.002)
                                                                                                              
--------------------------------------------------------------------------------------------------------------
Observations                     122     122     122     122     122     122     122     122     122     122  
R2                             0.0002   0.021   0.021   0.001   0.001  0.0003  0.00000 0.0002   0.002  0.0002 
Adjusted R2                    -0.008   0.013   0.013  -0.007  -0.008  -0.008  -0.008  -0.008  -0.006  -0.008 
Residual Std. Error (df = 120)  0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016 
F Statistic (df = 1; 120)       0.019   2.567   2.599   0.129   0.086   0.041  0.0003   0.019   0.272   0.022 
==============================================================================================================
Note:                                                                              *p<0.1; **p<0.05; ***p<0.01
stargazer(lmangry, lmanticipation, lmdisgust, lmfear, lmjoy, lmsadness, lmsurprise, lmtrust, lmpositive, lmnegative,  type="text", title="Emotion Analysis:Linear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Linear Regression Result.txt")

Emotion Analysis:Linear Regression Result
==============================================================================================================
                                                             Dependent variable:                              
                               -------------------------------------------------------------------------------
                                                                 DailyChange                                  
                                 (1)     (2)     (3)     (4)     (5)     (6)     (7)     (8)     (9)    (10)  
--------------------------------------------------------------------------------------------------------------
angryf                         -0.002                                                                         
                               (0.025)                                                                        
                                                                                                              
anticipationf                           0.031                                                                 
                                       (0.022)                                                                
                                                                                                              
disgustf                                       -0.049                                                         
                                               (0.029)                                                        
                                                                                                              
fearf                                                  -0.012                                                 
                                                       (0.024)                                                
                                                                                                              
joyf                                                            0.004                                         
                                                               (0.023)                                        
                                                                                                              
sadnessf                                                                0.007                                 
                                                                       (0.042)                                
                                                                                                              
surprisef                                                                      -0.004                         
                                                                               (0.024)                        
                                                                                                              
trustf                                                                                 -0.008                 
                                                                                       (0.021)                
                                                                                                              
positivef                                                                                       0.007         
                                                                                               (0.017)        
                                                                                                              
negativef                                                                                               0.001 
                                                                                                       (0.018)
                                                                                                              
Constant                        0.002  -0.0001  0.002   0.002   0.002   0.002   0.002   0.002   0.001   0.002 
                               (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.002)
                                                                                                              
--------------------------------------------------------------------------------------------------------------
Observations                     122     122     122     122     122     122     122     122     122     122  
R2                             0.00003  0.017   0.022   0.002  0.0003  0.0002  0.0002   0.001   0.002  0.00001
Adjusted R2                    -0.008   0.008   0.014  -0.006  -0.008  -0.008  -0.008  -0.007  -0.007  -0.008 
Residual Std. Error (df = 120)  0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016   0.016 
F Statistic (df = 1; 120)       0.004   2.019   2.742   0.261   0.035   0.029   0.029   0.125   0.198   0.002 
==============================================================================================================
Note:                                                                              *p<0.1; **p<0.05; ***p<0.01

Prepare corpus for identifying emotions.

For tweets that are associated with daily stock price increase/decrease:

iTesla<-Tesla %>%
  filter(DailyChange>0)
dTesla<-Tesla %>%
  filter(DailyChange<0)
#Increasing daily stock prices
doc_id<-c(1:80)
line<-c(rep(1,80))
text<-data.frame(doc_id, text=c(iTesla$tweet),line, stringsAsFactors=FALSE)
i_df_source<-DataframeSource(text)
i_df_nrc<-VCorpus(i_df_source)
i_nrc_clean<-clean_corpus(i_df_nrc)
i_nrc_dtm<-DocumentTermMatrix(i_nrc_clean)
i_nrc_m<-as.matrix(i_nrc_dtm)
i_nrc_td<-tidy(i_nrc_dtm)
i_nrc_tf_idf<-i_nrc_td %>%
  bind_tf_idf(term, document, count)
i_nrc_tf_idf<-i_nrc_tf_idf %>%
  arrange(desc(tf_idf))
i_nrc_tf_idf
#Decreasing daily stock prices
doc_id<-c(1:46)
line<-c(rep(1,46))
text<-data.frame(doc_id, text=c(dTesla$tweet),line, stringsAsFactors=FALSE)
d_df_source<-DataframeSource(text)
d_df_nrc<-VCorpus(d_df_source)
d_nrc_clean<-clean_corpus(d_df_nrc)
d_nrc_dtm<-DocumentTermMatrix(d_nrc_clean)
d_nrc_m<-as.matrix(d_nrc_dtm)
d_nrc_td<-tidy(d_nrc_dtm)
d_nrc_tf_idf<-d_nrc_td %>%
  bind_tf_idf(term, document, count)
d_nrc_tf_idf<-d_nrc_tf_idf %>%
  arrange(desc(tf_idf))
d_nrc_tf_idf

Angry

Find tf_idf score for angry words used in the tweets.

ianger<-i_nrc_tf_idf %>%
  filter(term %in% nrc_anger$word) %>%
  select(term,tf_idf)
ianger<-rename(ianger, i_tf_idf=tf_idf)
danger<-d_nrc_tf_idf %>%
  filter(term %in% nrc_anger$word) %>%
  select(term,tf_idf)
danger<-rename(danger, d_tf_idf=tf_idf)
angerwords<-full_join(ianger, danger,by="term")
#Replace all NA values as 0
angerwords$i_tf_idf[is.na(angerwords$i_tf_idf)]<-0
angerwords$d_tf_idf[is.na(angerwords$d_tf_idf)]<-0
angerwords

Visualize the relationship between the use of words from the angry category and stock price change.

plot2<-angerwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(angry words) in tweets associated with stock price increase", y="f(angry words) in tweets associated with stock price decrease", title="Frequency of angry words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))#+
  #facet_zoom(x=i_tf_idf<=0.5, y=d_tf_idf<=0.5)
plot2

#ggsave('angry diff.png')

Anticipation

Find tf_idf score for anticipation words used in the tweets.

ianticipation<-i_nrc_tf_idf %>%
  filter(term %in% nrc_anticipation$word) %>%
  select(term,tf_idf)
ianticipation<-rename(ianticipation, i_tf_idf=tf_idf)
danticipation<-d_nrc_tf_idf %>%
  filter(term %in% nrc_anticipation$word) %>%
  select(term,tf_idf)
danticipation<-rename(danticipation, d_tf_idf=tf_idf)
anticipationwords<-full_join(ianticipation, danticipation,by="term")
#Replace all NA values as 0
anticipationwords$i_tf_idf[is.na(anticipationwords$i_tf_idf)]<-0
anticipationwords$d_tf_idf[is.na(anticipationwords$d_tf_idf)]<-0
anticipationwords

Visualize the relationship between the use of words from the anticipation category and stock price change.

plot3<-anticipationwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(anticipation words) in tweets associated with stock price increase", y="f(anticipation words) in tweets associated with stock price decrease", title="Frequency of anticipation words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot3

#ggsave('anticipation diff.png')

Disgust

Find tf_idf score for disgust words used in the tweets.

idisgust<-i_nrc_tf_idf %>%
  filter(term %in% nrc_disgust$word) %>%
  select(term,tf_idf)
idisgust<-rename(idisgust, i_tf_idf=tf_idf)
ddisgust<-d_nrc_tf_idf %>%
  filter(term %in% nrc_disgust$word) %>%
  select(term,tf_idf)
ddisgust<-rename(ddisgust, d_tf_idf=tf_idf)
disgustwords<-full_join(idisgust, ddisgust,by="term")
#Replace all NA values as 0
disgustwords$i_tf_idf[is.na(disgustwords$i_tf_idf)]<-0
disgustwords$d_tf_idf[is.na(disgustwords$d_tf_idf)]<-0
disgustwords

Visualize the relationship between the use of words from the disgust category and stock price change

plot4<-disgustwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(disgust words) in tweets associated with stock price increase", y="f(disgust words) in tweets associated with stock price decrease", title="Frequency of disgust words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot4

#ggsave('disgust diff.png')

Fear

Find tf_idf score for fear words used in the tweets.

ifear<-i_nrc_tf_idf %>%
  filter(term %in% nrc_fear$word) %>%
  select(term,tf_idf)
ifear<-rename(ifear, i_tf_idf=tf_idf)
dfear<-d_nrc_tf_idf %>%
  filter(term %in% nrc_fear$word) %>%
  select(term,tf_idf)
dfear<-rename(dfear, d_tf_idf=tf_idf)
fearwords<-full_join(ifear, dfear,by="term")
#Replace all NA values as 0
fearwords$i_tf_idf[is.na(fearwords$i_tf_idf)]<-0
fearwords$d_tf_idf[is.na(fearwords$d_tf_idf)]<-0
fearwords

Visualize the relationship between the use of words from the fear category and stock price change.

plot5<-fearwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(fear words) in tweets associated with stock price increase", y="f(fear words) in tweets associated with stock price decrease", title="Frequency of fear words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot5

#ggsave('fear diff.png')

Joy

Find tf_idf score for joy words used in the tweets.

ijoy<-i_nrc_tf_idf %>%
  filter(term %in% nrc_joy$word) %>%
  select(term,tf_idf)
ijoy<-rename(ijoy, i_tf_idf=tf_idf)
djoy<-d_nrc_tf_idf %>%
  filter(term %in% nrc_joy$word) %>%
  select(term,tf_idf)
djoy<-rename(djoy, d_tf_idf=tf_idf)
joywords<-full_join(ijoy, djoy,by="term")
#Replace all NA values as 0
joywords$i_tf_idf[is.na(joywords$i_tf_idf)]<-0
joywords$d_tf_idf[is.na(joywords$d_tf_idf)]<-0
joywords

Visualize the relationship between the use of words from the joy category and stock price change.

plot6<-joywords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(joy words) in tweets associated with stock price increase", y="f(joy words) in tweets associated with stock price decrease", title="Frequency of joy words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot6

#ggsave('joy diff.png')

Sadness

Find tf_idf score for sadness words used in the tweets.

isadness<-i_nrc_tf_idf %>%
  filter(term %in% nrc_sadness$word) %>%
  select(term,tf_idf)
isadness<-rename(isadness, i_tf_idf=tf_idf)
dsadness<-d_nrc_tf_idf %>%
  filter(term %in% nrc_sadness$word) %>%
  select(term,tf_idf)
dsadness<-rename(dsadness, d_tf_idf=tf_idf)
sadnesswords<-full_join(isadness, dsadness,by="term")
#Replace all NA values as 0
sadnesswords$i_tf_idf[is.na(sadnesswords$i_tf_idf)]<-0
sadnesswords$d_tf_idf[is.na(sadnesswords$d_tf_idf)]<-0
sadnesswords

Visualize the relationship between the use of words from the sadness category and stock price change.

plot7<-sadnesswords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(sadness words) in tweets associated with stock price increase", y="f(sadness words) in tweets associated with stock price decrease", title="Frequency of sadness words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2.5), breaks=seq(0,2.5,0.5))+
  scale_y_continuous(limits=c(0,2.5), breaks=seq(0,2.5,0.5))
plot7

#ggsave('sadness diff.png')

Surprise

Find tf_idf score for surprise words used in the tweets.

isurprise<-i_nrc_tf_idf %>%
  filter(term %in% nrc_surprise$word) %>%
  select(term,tf_idf)
isurprise<-rename(isurprise, i_tf_idf=tf_idf)
dsurprise<-d_nrc_tf_idf %>%
  filter(term %in% nrc_surprise$word) %>%
  select(term,tf_idf)
dsurprise<-rename(dsurprise, d_tf_idf=tf_idf)
surprisewords<-full_join(isurprise, dsurprise,by="term")
#Replace all NA values as 0
surprisewords$i_tf_idf[is.na(surprisewords$i_tf_idf)]<-0
surprisewords$d_tf_idf[is.na(surprisewords$d_tf_idf)]<-0
surprisewords

Visualize the relationship between the use of words from the surprise category and stock price change.

plot8<-surprisewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(surprise words) in tweets associated with stock price increase", y="f(surprise words) in tweets associated with stock price decrease", title="Frequency of surprise words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot8

#ggsave('surprise diff.png')

Trust

Find tf_idf score for trust words used in the tweets.

itrust<-i_nrc_tf_idf %>%
  filter(term %in% nrc_trust$word) %>%
  select(term,tf_idf)
itrust<-rename(itrust, i_tf_idf=tf_idf)
dtrust<-d_nrc_tf_idf %>%
  filter(term %in% nrc_trust$word) %>%
  select(term,tf_idf)
dtrust<-rename(dtrust, d_tf_idf=tf_idf)
trustwords<-full_join(itrust, dtrust,by="term")
#Replace all NA values as 0
trustwords$i_tf_idf[is.na(trustwords$i_tf_idf)]<-0
trustwords$d_tf_idf[is.na(trustwords$d_tf_idf)]<-0
trustwords

Visualize the relationship between the use of words from the trust category and stock price change.

plot9<-trustwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(trust words) in tweets associated with stock price increase", y="f(trust words) in tweets associated with stock price decrease", title="Frequency of trust words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot9

#ggsave('trust diff.png')

Negative

Find tf_idf score for negative words used in the tweets.

inegative<-i_nrc_tf_idf %>%
  filter(term %in% nrc_negative$word) %>%
  select(term,tf_idf)
inegative<-rename(inegative, i_tf_idf=tf_idf)
dnegative<-d_nrc_tf_idf %>%
  filter(term %in% nrc_negative$word) %>%
  select(term,tf_idf)
dnegative<-rename(dnegative, d_tf_idf=tf_idf)
negativewords<-full_join(inegative, dnegative,by="term")
#Replace all NA values as 0
negativewords$i_tf_idf[is.na(negativewords$i_tf_idf)]<-0
negativewords$d_tf_idf[is.na(negativewords$d_tf_idf)]<-0
negativewords

Visualize the relationship between the use of words from the negative category and stock price change.

plot10<-negativewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(negative words) in tweets associated with stock price increase", y="f(negative words) in tweets associated with stock price decrease", title="Frequency of negative words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot10

#ggsave('negative diff.png')

Positive

Find tf_idf score for positive words used in the tweets.

ipositive<-i_nrc_tf_idf %>%
  filter(term %in% nrc_positive$word) %>%
  select(term,tf_idf)
ipositive<-rename(ipositive, i_tf_idf=tf_idf)
dpositive<-d_nrc_tf_idf %>%
  filter(term %in% nrc_positive$word) %>%
  select(term,tf_idf)
dpositive<-rename(dpositive, d_tf_idf=tf_idf)
positivewords<-full_join(ipositive, dpositive,by="term")
#Replace all NA values as 0
positivewords$i_tf_idf[is.na(positivewords$i_tf_idf)]<-0
positivewords$d_tf_idf[is.na(positivewords$d_tf_idf)]<-0
positivewords

Visualize the relationship between the use of words from the positive category and stock price change.

plot11<-positivewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(positive words) in tweets associated with stock price increase", y="f(positive words) in tweets associated with stock price decrease", title="Frequency of positive words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot11

#ggsave('positive diff.png')

Logistic Regression

emotionsfrequency$Direction[emotionsfrequency$DailyChange>0]<-1
emotionsfrequency$Direction[emotionsfrequency$DailyChange==0]<-0
emotionsfrequency$Direction[emotionsfrequency$DailyChange<0]<--1
emotionsfrequency$Direction<-as.factor(emotionsfrequency$Direction)
emotionsfrequency
logit<-glm(Direction ~ angryf+anticipationf+disgustf+fearf+joyf+sadnessf+surprisef+trustf+negativef+positivef, emotionsfrequency, family=binomial(link ="logit"))
summary(logit)

Call:
glm(formula = Direction ~ angryf + anticipationf + disgustf + 
    fearf + joyf + sadnessf + surprisef + trustf + negativef + 
    positivef, family = binomial(link = "logit"), data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3098  -1.2078   0.7149   0.9781   1.4492  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)  
(Intercept)    -0.01673    0.43064  -0.039   0.9690  
angryf          5.29635    4.64536   1.140   0.2542  
anticipationf  -1.80057    4.56033  -0.395   0.6930  
disgustf       -7.02682    7.42839  -0.946   0.3442  
fearf          -4.87369    5.31455  -0.917   0.3591  
joyf          -10.99557    6.02581  -1.825   0.0680 .
sadnessf        5.32096    7.75805   0.686   0.4928  
surprisef      12.01708    6.88109   1.746   0.0807 .
trustf          1.64431    3.75967   0.437   0.6619  
negativef      -2.78715    4.59601  -0.606   0.5442  
positivef       6.67718    3.41520   1.955   0.0506 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 150.91  on 111  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 172.91

Number of Fisher Scoring iterations: 4
stargazer(logit, type="text", title="Emotion Analysis: Multi-logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multi-logistic Regression Result.txt")

Emotion Analysis: Multi-logistic Regression Result
=============================================
                      Dependent variable:    
                  ---------------------------
                           Direction         
---------------------------------------------
angryf                       5.296           
                            (4.645)          
                                             
anticipationf               -1.801           
                            (4.560)          
                                             
disgustf                    -7.027           
                            (7.428)          
                                             
fearf                       -4.874           
                            (5.315)          
                                             
joyf                       -10.996*          
                            (6.026)          
                                             
sadnessf                     5.321           
                            (7.758)          
                                             
surprisef                   12.017*          
                            (6.881)          
                                             
trustf                       1.644           
                            (3.760)          
                                             
negativef                   -2.787           
                            (4.596)          
                                             
positivef                   6.677*           
                            (3.415)          
                                             
Constant                    -0.017           
                            (0.431)          
                                             
---------------------------------------------
Observations                  122            
Log Likelihood              -75.453          
Akaike Inf. Crit.           172.905          
=============================================
Note:             *p<0.1; **p<0.05; ***p<0.01
llogit<-glm(Direction ~ langryf+lanticipationf+ldisgustf+lfearf+ljoyf+lsadnessf+lsurprisef+ltrustf+lpositivef+lnegativef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogit)

Call:
glm(formula = Direction ~ langryf + lanticipationf + ldisgustf + 
    lfearf + ljoyf + lsadnessf + lsurprisef + ltrustf + lpositivef + 
    lnegativef, family = binomial(link = "logit"), data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3249  -1.2087   0.7374   0.9890   1.4407  

Coefficients:
                Estimate Std. Error z value Pr(>|z|)  
(Intercept)     -0.04333    0.44553  -0.097   0.9225  
langryf          5.64730    5.06677   1.115   0.2650  
lanticipationf  -2.37994    4.90886  -0.485   0.6278  
ldisgustf       -7.20911    8.11123  -0.889   0.3741  
lfearf          -5.45094    5.79439  -0.941   0.3468  
ljoyf          -11.35046    6.49781  -1.747   0.0807 .
lsadnessf        4.86229    7.98701   0.609   0.5427  
lsurprisef      12.81309    7.39783   1.732   0.0833 .
ltrustf          2.32667    4.22383   0.551   0.5817  
lpositivef       7.34769    3.84762   1.910   0.0562 .
lnegativef      -2.87934    5.00563  -0.575   0.5651  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 151.42  on 111  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 173.42

Number of Fisher Scoring iterations: 4
stargazer(llogit, type="text", title="Emotion Analysis: Multi-logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multi-logistic Regression Result (Log).txt")

Emotion Analysis: Multi-logistic Regression Result
=============================================
                      Dependent variable:    
                  ---------------------------
                           Direction         
---------------------------------------------
langryf                      5.647           
                            (5.067)          
                                             
lanticipationf              -2.380           
                            (4.909)          
                                             
ldisgustf                   -7.209           
                            (8.111)          
                                             
lfearf                      -5.451           
                            (5.794)          
                                             
ljoyf                      -11.350*          
                            (6.498)          
                                             
lsadnessf                    4.862           
                            (7.987)          
                                             
lsurprisef                  12.813*          
                            (7.398)          
                                             
ltrustf                      2.327           
                            (4.224)          
                                             
lpositivef                  7.348*           
                            (3.848)          
                                             
lnegativef                  -2.879           
                            (5.006)          
                                             
Constant                    -0.043           
                            (0.446)          
                                             
---------------------------------------------
Observations                  122            
Log Likelihood              -75.710          
Akaike Inf. Crit.           173.421          
=============================================
Note:             *p<0.1; **p<0.05; ***p<0.01
logitangry<-glm(Direction ~ angryf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitangry)

Call:
glm(formula = Direction ~ angryf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6385  -1.3782   0.9384   0.9891   0.9891  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.4606     0.2103   2.190   0.0285 *
angryf        2.8950     3.8212   0.758   0.4487  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 159.97  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 163.97

Number of Fisher Scoring iterations: 4
llogitangry<-glm(Direction ~ langryf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitangry)

Call:
glm(formula = Direction ~ langryf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6289  -1.3782   0.9367   0.9891   0.9891  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.4606     0.2126   2.167   0.0302 *
langryf       3.0586     4.2034   0.728   0.4668  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.04  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.04

Number of Fisher Scoring iterations: 4
logitanticipation<-glm(Direction ~ anticipationf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitanticipation)

Call:
glm(formula = Direction ~ anticipationf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4421  -1.4099   0.9341   0.9640   1.0490  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)  
(Intercept)     0.6036     0.2538   2.378   0.0174 *
anticipationf  -1.0774     2.7472  -0.392   0.6949  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.48  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.48

Number of Fisher Scoring iterations: 4
llogitanticipation<-glm(Direction ~ lanticipationf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitanticipation)

Call:
glm(formula = Direction ~ lanticipationf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4346  -1.4097   0.9404   0.9635   1.0211  

Coefficients:
               Estimate Std. Error z value Pr(>|z|)  
(Intercept)      0.5869     0.2621   2.239   0.0251 *
lanticipationf  -0.8609     3.1441  -0.274   0.7842  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.56  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.56

Number of Fisher Scoring iterations: 4
logitdisgust<-glm(Direction ~ disgustf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitdisgust)

Call:
glm(formula = Direction ~ disgustf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4294  -1.4294   0.9448   0.9448   1.1855  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.5754     0.1943   2.961  0.00306 **
disgustf     -2.9723     3.9005  -0.762  0.44603   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.00  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164

Number of Fisher Scoring iterations: 4
llogitdisgust<-glm(Direction ~ ldisgustf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitdisgust)

Call:
glm(formula = Direction ~ ldisgustf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4292  -1.4292   0.9449   0.9449   1.1806  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.5749     0.1953   2.944  0.00324 **
ldisgustf    -3.1942     4.5120  -0.708  0.47898   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.11  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.11

Number of Fisher Scoring iterations: 4
logitfear<-glm(Direction ~ fearf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitfear)

Call:
glm(formula = Direction ~ fearf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4421  -1.4137   0.9340   0.9528   1.1274  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.6037     0.2181   2.767  0.00565 **
fearf        -1.7782     2.9283  -0.607  0.54369   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.27  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.27

Number of Fisher Scoring iterations: 4
llogitfear<-glm(Direction ~ lfearf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitfear)

Call:
glm(formula = Direction ~ lfearf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4404  -1.4123   0.9354   0.9541   1.1070  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.5999     0.2224   2.698  0.00698 **
lfearf       -1.7908     3.3554  -0.534  0.59353   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.35  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.35

Number of Fisher Scoring iterations: 4
logitjoy<-glm(Direction ~ joyf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitjoy)

Call:
glm(formula = Direction ~ joyf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4558  -1.4104   0.9225   0.9605   1.1476  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.6343     0.2311   2.745  0.00605 **
joyf         -2.0669     2.8293  -0.731  0.46507   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.10  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.1

Number of Fisher Scoring iterations: 4
llogitjoy<-glm(Direction ~ ljoyf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitjoy)

Call:
glm(formula = Direction ~ ljoyf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4502  -1.4090   0.9272   0.9617   1.1106  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)   0.6216     0.2368   2.625  0.00866 **
ljoyf        -1.9173     3.2332  -0.593  0.55319   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.29  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.29

Number of Fisher Scoring iterations: 4
logitsadness<-glm(Direction ~ sadnessf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsadness)

Call:
glm(formula = Direction ~ sadnessf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4124  -1.4123   0.9593   0.9594   0.9597  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)  
(Intercept)  0.537309   0.222767   2.412   0.0159 *
sadnessf    -0.007262   5.264444  -0.001   0.9989  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.64  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.64

Number of Fisher Scoring iterations: 4
llogitsadness<-glm(Direction ~ lsadnessf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsadness)

Call:
glm(formula = Direction ~ lsadnessf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4408  -1.4092   0.9569   0.9621   0.9621  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.5302     0.2246   2.360   0.0183 *
lsadnessf     0.3162     5.6432   0.056   0.9553  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.63  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.63

Number of Fisher Scoring iterations: 4
logitsurprise<-glm(Direction ~ surprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsurprise)

Call:
glm(formula = Direction ~ surprisef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4135  -1.4127   0.9584   0.9591   0.9644  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)  0.53963    0.21068   2.561   0.0104 *
surprisef   -0.07691    2.95653  -0.026   0.9792  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.63  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.63

Number of Fisher Scoring iterations: 4
llogitsurprise<-glm(Direction ~ lsurprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsurprise)

Call:
glm(formula = Direction ~ lsurprisef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4838  -1.4066   0.9538   0.9643   0.9643  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.5244     0.2138   2.453   0.0142 *
lsurprisef    0.4246     3.4371   0.124   0.9017  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.62  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.62

Number of Fisher Scoring iterations: 4
logitsurprise<-glm(Direction ~ surprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsurprise)

Call:
glm(formula = Direction ~ surprisef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4135  -1.4127   0.9584   0.9591   0.9644  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)  0.53963    0.21068   2.561   0.0104 *
surprisef   -0.07691    2.95653  -0.026   0.9792  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.63  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.63

Number of Fisher Scoring iterations: 4
llogitsurprise<-glm(Direction ~ lsurprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsurprise)

Call:
glm(formula = Direction ~ lsurprisef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4838  -1.4066   0.9538   0.9643   0.9643  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.5244     0.2138   2.453   0.0142 *
lsurprisef    0.4246     3.4371   0.124   0.9017  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.62  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.62

Number of Fisher Scoring iterations: 4
logittrust<-glm(Direction ~ trustf, emotionsfrequency, family=binomial(link ="logit"))
summary(logittrust)

Call:
glm(formula = Direction ~ trustf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7021  -1.3718   0.9348   0.9661   1.0155  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.3936     0.2869   1.372    0.170
trustf        1.8369     2.8227   0.651    0.515

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.20  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.2

Number of Fisher Scoring iterations: 4
llogittrust<-glm(Direction ~ ltrustf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogittrust)

Call:
glm(formula = Direction ~ ltrustf, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7287  -1.3627   0.9272   0.9659   1.0305  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.3558     0.2963   1.201    0.230
ltrustf       2.4791     3.1917   0.777    0.437

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.01  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.01

Number of Fisher Scoring iterations: 4
logitnegative<-glm(Direction ~ negativef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitnegative)

Call:
glm(formula = Direction ~ negativef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4200  -1.4105   0.9528   0.9587   1.0087  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.5543     0.2311   2.399   0.0165 *
negativef    -0.2870     2.2472  -0.128   0.8984  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.62  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.62

Number of Fisher Scoring iterations: 4
llogitnegative<-glm(Direction ~ lnegativef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitnegative)

Call:
glm(formula = Direction ~ lnegativef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.4186  -1.4105   0.9540   0.9591   0.9936  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)  
(Intercept)   0.5510     0.2371   2.324   0.0201 *
lnegativef   -0.2516     2.6136  -0.096   0.9233  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 160.63  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 164.63

Number of Fisher Scoring iterations: 4
logitpositive<-glm(Direction ~ positivef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitpositive)

Call:
glm(formula = Direction ~ positivef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7453  -1.3783   0.9083   0.9725   1.0591  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.2848     0.3308   0.861    0.389
positivef     1.9847     2.1788   0.911    0.362

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 159.78  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 163.78

Number of Fisher Scoring iterations: 4
llogitpositive<-glm(Direction ~ lpositivef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitpositive)

Call:
glm(formula = Direction ~ lpositivef, family = binomial(link = "logit"), 
    data = emotionsfrequency)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7351  -1.3774   0.9015   0.9711   1.0742  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)   0.2477     0.3430   0.722    0.470
lpositivef    2.4826     2.5023   0.992    0.321

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 160.64  on 121  degrees of freedom
Residual deviance: 159.63  on 120  degrees of freedom
  (4 observations deleted due to missingness)
AIC: 163.63

Number of Fisher Scoring iterations: 4
stargazer(logitangry, logitanticipation, logitdisgust, logitfear, logitjoy, logitsadness, logitsurprise, logittrust, logitpositive, logitnegative,  type="text", title="Emotion Analysis: Logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Logistic Regression Result.txt")

Emotion Analysis: Logistic Regression Result
====================================================================================================
                                                 Dependent variable:                                
                  ----------------------------------------------------------------------------------
                                                      Direction                                     
                    (1)     (2)     (3)      (4)      (5)      (6)     (7)     (8)     (9)    (10)  
----------------------------------------------------------------------------------------------------
angryf             2.895                                                                            
                  (3.821)                                                                           
                                                                                                    
anticipationf             -1.077                                                                    
                          (2.747)                                                                   
                                                                                                    
disgustf                           -2.972                                                           
                                  (3.900)                                                           
                                                                                                    
fearf                                       -1.778                                                  
                                           (2.928)                                                  
                                                                                                    
joyf                                                 -2.067                                         
                                                    (2.829)                                         
                                                                                                    
sadnessf                                                     -0.007                                 
                                                             (5.264)                                
                                                                                                    
surprisef                                                            -0.077                         
                                                                     (2.957)                        
                                                                                                    
trustf                                                                        1.837                 
                                                                             (2.823)                
                                                                                                    
positivef                                                                             1.985         
                                                                                     (2.179)        
                                                                                                    
negativef                                                                                    -0.287 
                                                                                             (2.247)
                                                                                                    
Constant          0.461** 0.604** 0.575*** 0.604*** 0.634*** 0.537** 0.540**  0.394   0.285  0.554**
                  (0.210) (0.254) (0.194)  (0.218)  (0.231)  (0.223) (0.211) (0.287) (0.331) (0.231)
                                                                                                    
----------------------------------------------------------------------------------------------------
Observations        122     122     122      122      122      122     122     122     122     122  
Log Likelihood    -79.984 -80.241 -79.998  -80.133  -80.049  -80.318 -80.317 -80.099 -79.891 -80.310
Akaike Inf. Crit. 163.967 164.483 163.997  164.266  164.098  164.635 164.635 164.198 163.782 164.619
====================================================================================================
Note:                                                                    *p<0.1; **p<0.05; ***p<0.01
stargazer(llogitangry, llogitanticipation, llogitdisgust, llogitfear, llogitjoy, llogitsadness, llogitsurprise, llogittrust, llogitpositive, llogitnegative,  type="text", title="Emotion Analysis: Logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Logistic Regression Result (Log).txt")

Emotion Analysis: Logistic Regression Result
====================================================================================================
                                                 Dependent variable:                                
                  ----------------------------------------------------------------------------------
                                                      Direction                                     
                    (1)     (2)     (3)      (4)      (5)      (6)     (7)     (8)     (9)    (10)  
----------------------------------------------------------------------------------------------------
langryf            3.059                                                                            
                  (4.203)                                                                           
                                                                                                    
lanticipationf            -0.861                                                                    
                          (3.144)                                                                   
                                                                                                    
ldisgustf                          -3.194                                                           
                                  (4.512)                                                           
                                                                                                    
lfearf                                      -1.791                                                  
                                           (3.355)                                                  
                                                                                                    
ljoyf                                                -1.917                                         
                                                    (3.233)                                         
                                                                                                    
lsadnessf                                                     0.316                                 
                                                             (5.643)                                
                                                                                                    
lsurprisef                                                            0.425                         
                                                                     (3.437)                        
                                                                                                    
ltrustf                                                                       2.479                 
                                                                             (3.192)                
                                                                                                    
lpositivef                                                                            2.483         
                                                                                     (2.502)        
                                                                                                    
lnegativef                                                                                   -0.252 
                                                                                             (2.614)
                                                                                                    
Constant          0.461** 0.587** 0.575*** 0.600*** 0.622*** 0.530** 0.524**  0.356   0.248  0.551**
                  (0.213) (0.262) (0.195)  (0.222)  (0.237)  (0.225) (0.214) (0.296) (0.343) (0.237)
                                                                                                    
----------------------------------------------------------------------------------------------------
Observations        122     122     122      122      122      122     122     122     122     122  
Log Likelihood    -80.021 -80.280 -80.053  -80.176  -80.143  -80.316 -80.310 -80.006 -79.814 -80.313
Akaike Inf. Crit. 164.043 164.561 164.105  164.352  164.285  164.632 164.620 164.011 163.628 164.626
====================================================================================================
Note:                                                                    *p<0.1; **p<0.05; ***p<0.01
---
title: "Sentiment Analysis for Tesla Tweets & Relationship with Daily Stock Price Change"
author: "Li Peishan"
date: "3/30/2021"
output:
  html_notebook:
    toc: yes
    theme: journal
---
<style>
body{ /* Normal */
font-size: 15px;
color: black;
}
write {  
line-height: 7em;
}
table { /* Table */
font-size: 12px;
}
h1 { /* Header 1 */
font-size: 30px;
}
h2 { /* Header 2 *
font-size: 26px;
}
h3 { /* Header 3 */
font-size: 22px;
}
code.r{ /* Code block */
font-size: 14px;
}
pre { /* Code block */
font-size: 14px
}
.main-container {
    width: 80%;
    max-width: unset;
}
</style>

```{r setup, echo=TRUE, eval=TRUE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = TRUE,eval=TRUE, message=FALSE, warning=FALSE)
```

Load packages:
```{r, echo=TRUE, eval=TRUE}
library(readxl)
library(magrittr)
library(dplyr)
library(ggplot2)    
library(ggmap)
library(ggthemes)
library(ggpubr)
library(ggforce)
library(tidyverse)
library(gmodels)
library(rgdal)
library(osmdata)
library(nominatim)
library(jsonlite)
library(RColorBrewer)
library(tidyr)
library(leaflet)
library(maps)
library(tigris)
library(tidytext)
library(textdata)
library(tm)
library(quanteda)
library(rvest)
library(stringr)
library(SnowballC)
library(wordcloud)
library(plotrix)
library(qdapDictionaries)
library(formattable)
library(stringr)
library(DT)
```

Import database:
```{r import data, echo=TRUE, eval=TRUE}
Tesla<-read.csv("/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/(update)TSLA_sentiment_analysis.csv")
```

# Cleaning the text and word cloud

## Text cleaning--Further remove numbers and punctuations

Create and preprocess text in corpus (162 tweets)
```{r create a dtm, echo=TRUE, eval=TRUE}
doc_id<-c(1:126)
line<-c(rep(1,126))
text<-data.frame(doc_id, text=Tesla$tweet,line, stringsAsFactors=FALSE)
df_source<-DataframeSource(text)
df_corpus<-VCorpus(df_source)
#Clean text
#removefullycap<-function(x){gsub("[A-Z][A-Z]+", " ", x)}
#removeemoji<-function(x){gsub("[^\x01-\x7F]", "", x)}
clean_corpus<-function(corpus){
  #corpus<-tm_map(corpus, content_transformer(removefullycap))
  #corpus<-tm_map(corpus, content_transformer(removeemoji))
  #corpus<-tm_map(corpus, content_transformer(tolower))
  #corpus<-tm_map(corpus, removeWords, c(stopwords("en")))
  corpus<-tm_map(corpus, removeNumbers)
  corpus<-tm_map(corpus, removePunctuation)
  corpus<-tm_map(corpus, stripWhitespace)
  return(corpus)
}
corpus_clean<-clean_corpus(df_corpus)
corpus_dtm<-DocumentTermMatrix(corpus_clean)
corpus_m<-as.matrix(corpus_dtm)
corpus_dtm
dim(corpus_m)
```

## Word cloud

Calculate tf_itf scores for tweets.
```{r frequncies, echo=TRUE, eval=TRUE}
#Tidy objects
corpustd<-tidy(corpus_dtm)
#Calculate frequencies
corpustf_idf<-corpustd %>%
  bind_tf_idf(term, document, count) %>%
  arrange(desc(tf_idf))
corpustf_idf
```

Word cloud for Tesla CEO's tweets
```{r word clouds, echo=TRUE, eval=TRUE}
purple_orange<-brewer.pal(10, "PuOr")
purple_orange<-purple_orange[-(1:2)]
set.seed(2200)
wordcloud(corpustf_idf$term, corpustf_idf$tf, max.words=100, colors=purple_orange)
```

# Sentiment

## (a) Stay positive

Calculate the tone of each text based on the positive and negative words that are being used in the tweets.

First build the sentiment function. Obtain the list of `positive.words` and `negative.words` from the sentiment dictionary of Hu & Liu (2004) using the `qdapDictionaries` package.
```{r sentiment function, echo=TRUE, eval=TRUE}
sentiment<-function(words=c("really great good stuff bad")){
  tok<-tokens(words)
  pos.count<-sum(tok[[1]] %in% positive.words)
  #cat("\n positive words:",tok[[1]][which(tok[[1]]%in%positive.words)],"\n")
  neg.count<-sum(tok[[1]]%in%negative.words)
  #cat("\n negative words:",tok[[1]][which(tok[[1]]%in%negative.words)],"\n")
  out<-(pos.count-neg.count)/(pos.count+neg.count)
  #cat("\n Tone of Document:",out)
  return(out)
}
```

Apply the function on text of the 162 tweets.
```{r tone of document, echo=TRUE, eval=TRUE}
toneofdocument<-Tesla
toneofdocument<-toneofdocument
for (i in 1:126){
  toneofdocument$toneofdocument[i]<-sentiment(toneofdocument$tweet[i])
}
toneofdocument
```

```{r write csv, echo=TRUE, eval=TRUE}
write.csv(toneofdocument,"/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Tesla tone of document.csv", row.names = FALSE)
```

Visualize the relationship between tone of the document and daily stock price change.
```{r visualize the relationship, echo=TRUE, eval=TRUE, fig.height=3,fig.width=5}
plot<-toneofdocument %>%
  ggplot(aes(x=toneofdocument,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Tone of Document", y="Daily stock price change", title="Relationship between Tone of Document and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5, angle=45,size=4.5))+
  coord_flip()
plot
```

## (b) Positive VS Negative

Ignore all documents that do not have words contained within the Hu & Liu dictionary and all neutral scores. Collapse the positive and negative texts into two larger documents.
```{r segregate, echo=TRUE, eval=TRUE}
positivetone<-toneofdocument %>%
  filter(toneofdocument>0) %>%
  select(tweet)
positivetext<-paste(unlist(positivetone$tweet), collapse=" ")
negativetone<-toneofdocument %>%
  filter(toneofdocument<0) %>%
  select(tweet)
negativetext<-paste(unlist(negativetone$tweet), collapse=" ")
```

### Comparison Wordcloud

Generate comparison cloud showing the most-frequent positive and negative words, where document 1 denotes positive texts, and document 2 denotes negative texts.
```{r comparison word cloud, echo=TRUE, eval=TRUE}
doc_id<-c("Frequent words in positive sentiment tweets","Frequent words in negative sentiment tweets")
line<-c(1,1)
comparisontext<-data.frame(doc_id, text=c(positivetext, negativetext),line, stringsAsFactors=FALSE)
df_source<-DataframeSource(comparisontext)
df_corpus<-VCorpus(df_source)
corpuscomparison_clean<-clean_corpus(df_corpus)
corpuscomparison_tdm<-TermDocumentMatrix(corpuscomparison_clean)
corpuscomparison_m<-as.matrix(corpuscomparison_tdm)
set.seed(2105)
comparison.cloud(corpuscomparison_m, colors=c("orange", "purple"), scale=c(0.1,2), title.size=1, max.words=100)
```

## (c) Get in their mind

Identify emotions (anger, anticipation, disgust, fear, sadness, sadness, surprise, trust) as well as negative and positive using the NRC Word-Emotion Association Lexicon in the `tidytext` package.
```{r NRC, echo=TRUE, eval=TRUE}
nrc_anger<-get_sentiments("nrc") %>% 
  filter(sentiment=="anger")
nrc_anticipation<-get_sentiments("nrc") %>% 
  filter(sentiment=="anticipation")
nrc_disgust<-get_sentiments("nrc") %>% 
  filter(sentiment=="disgust")
nrc_fear<-get_sentiments("nrc") %>% 
  filter(sentiment=="fear")
nrc_joy<-get_sentiments("nrc") %>% 
  filter(sentiment=="joy")
nrc_sadness<-get_sentiments("nrc") %>% 
  filter(sentiment=="sadness")
nrc_surprise<-get_sentiments("nrc") %>% 
  filter(sentiment=="surprise")
nrc_trust<-get_sentiments("nrc") %>% 
  filter(sentiment=="trust")
nrc_negative<-get_sentiments("nrc") %>% 
  filter(sentiment=="negative")
nrc_positive<-get_sentiments("nrc") %>% 
  filter(sentiment=="positive")
```

```{r}
nrc_anger$word
```

```{r}
angryf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  angercount<-sum(tok[[1]] %in% nrc_anger$word)
  angerf<-angercount/wordcount
  return(angerf)
}
anticipationf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  anticipationcount<-sum(tok[[1]] %in% nrc_anticipation$word)
  anticipationf<-anticipationcount/wordcount
  return(anticipationf)
}  
disgustf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  disgustcount<-sum(tok[[1]] %in% nrc_disgust$word)
  disgustf<-disgustcount/wordcount
  return(disgustf)
}
fearf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  fearcount<-sum(tok[[1]] %in% nrc_fear$word)
  fearf<-fearcount/wordcount
  return(fearf)
}
joyf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  joycount<-sum(tok[[1]] %in% nrc_joy$word)
  joyf<-joycount/wordcount
  return(joyf)
}
sadnessf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  sadnesscount<-sum(tok[[1]] %in% nrc_surprise$word)
  sadnessf<-sadnesscount/wordcount
  return(sadnessf)
}
surprisef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  surprisecount<-sum(tok[[1]] %in% nrc_sadness$word)
  surprisef<-surprisecount/wordcount
  return(surprisef)
}
trustf<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  trustcount<-sum(tok[[1]] %in% nrc_trust$word)
  trustf<-trustcount/wordcount
  return(trustf)
}
negativef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  negativecount<-sum(tok[[1]] %in% nrc_negative$word)
  negativef<-negativecount/wordcount
  return(negativef)
}
positivef<-function(words=c("Check out the frequency of words that represent emotions")){
  tok<-tokens(words)
  wordcount<-length(tok[[1]])
  positivecount<-sum(tok[[1]] %in% nrc_positive$word)
  positivef<-positivecount/wordcount
  return(positivef)
}
```

```{r}
emotionsfrequency<-Tesla
for (i in 1:126){
  emotionsfrequency$angryf[i]<-angryf(emotionsfrequency$tweet[i])
  emotionsfrequency$anticipationf[i]<-anticipationf(emotionsfrequency$tweet[i])
  emotionsfrequency$disgustf[i]<-disgustf(emotionsfrequency$tweet[i])
  emotionsfrequency$fearf[i]<-fearf(emotionsfrequency$tweet[i])
  emotionsfrequency$joyf[i]<-joyf(emotionsfrequency$tweet[i])
  emotionsfrequency$sadnessf[i]<-sadnessf(emotionsfrequency$tweet[i])
  emotionsfrequency$surprisef[i]<-surprisef(emotionsfrequency$tweet[i])
  emotionsfrequency$trustf[i]<-trustf(emotionsfrequency$tweet[i])
  emotionsfrequency$negativef[i]<-negativef(emotionsfrequency$tweet[i])
  emotionsfrequency$positivef[i]<-positivef(emotionsfrequency$tweet[i])
  
  emotionsfrequency$langryf[i]<-log(emotionsfrequency$angryf[i]+1)
  emotionsfrequency$lanticipationf[i]<-log(emotionsfrequency$anticipationf[i]+1)
  emotionsfrequency$ldisgustf[i]<-log(emotionsfrequency$disgustf[i]+1)
  emotionsfrequency$lfearf[i]<-log(emotionsfrequency$fearf[i]+1)
  emotionsfrequency$ljoyf[i]<-log(emotionsfrequency$joyf[i]+1)
  emotionsfrequency$lsadnessf[i]<-log(emotionsfrequency$sadnessf[i]+1)
  emotionsfrequency$lsurprisef[i]<-log(emotionsfrequency$surprisef[i]+1)
  emotionsfrequency$ltrustf[i]<-log(emotionsfrequency$trustf[i]+1)
  emotionsfrequency$lnegativef[i]<-log(emotionsfrequency$negativef[i]+1)
  emotionsfrequency$lpositivef[i]<-log(emotionsfrequency$positivef[i]+1)
}
emotionsfrequency
```

```{r write csv emotions, echo=TRUE, eval=TRUE}
write.csv(emotionsfrequency,"/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Tesla frequency of emotions.csv", row.names = FALSE)
```

```{r visualize the emotion relationship, echo=TRUE, eval=TRUE}
plotangry<-emotionsfrequency %>%
  ggplot(aes(x=langryf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of angry words in the text (Log)", y="Daily stock price change", title="Relationship between Angry emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotangry
#ggsave('Angry-linear.png')
```

```{r}
plotanticipation<-emotionsfrequency %>%
  ggplot(aes(x=lanticipationf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of anticipation words in the text (Log)", y="Daily stock price change", title="Relationship between Anticipation emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotanticipation
#ggsave('Anticipation-linear.png')
```

```{r}
plotdisgust<-emotionsfrequency %>%
  ggplot(aes(x=ldisgustf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of disgust words in the text (Log)", y="Daily stock price change", title="Relationship between Disgust emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotdisgust
#ggsave('Disgust-linear.png')
```

```{r}
plotjoy<-emotionsfrequency %>%
  ggplot(aes(x=ljoyf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of joy words in the text (Log)", y="Daily stock price change", title="Relationship between Joy emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotjoy
#ggsave('Joy-linear.png')
```

```{r}
plotfear<-emotionsfrequency %>%
  ggplot(aes(x=lfearf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of fear words in the text (Log)", y="Daily stock price change", title="Relationship between Fear emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotfear
#ggsave('Fear-linear.png')
```

```{r}
plotsadness<-emotionsfrequency %>%
  ggplot(aes(x=lsadnessf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of sadness words in the text (Log)", y="Daily stock price change", title="Relationship between Sadness emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotsadness
#ggsave('Sadness-linear.png')
```

```{r}
plotsurprise<-emotionsfrequency %>%
  ggplot(aes(x=lsurprisef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of sadness words in the text (Log)", y="Daily stock price change", title="Relationship between Surprise emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotsurprise
#ggsave('Surprise-linear.png')
```

```{r}
plottrust<-emotionsfrequency %>%
  ggplot(aes(x=ltrustf,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of trust words in the text (Log)", y="Daily stock price change", title="Relationship between Trust emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plottrust
#ggsave('Trust-linear.png')
```

```{r}
plotnegative<-emotionsfrequency %>%
  ggplot(aes(x=lnegativef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of negative words in the text (Log)", y="Daily stock price change", title="Relationship between Negative emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotnegative
#ggsave('Negative-linear.png')
```

```{r}
plotpositive<-emotionsfrequency %>%
  ggplot(aes(x=lpositivef,y=DailyChange))+
  geom_jitter(color="#00CED1")+
  geom_smooth(method="lm", se=FALSE, color="red")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="Frequency of positive words in the text (Log)", y="Daily stock price change", title="Relationship between Positive emotions and Daily Stock Price Change")+
  theme(plot.title=element_text(hjust=0.5))+
  theme(axis.text.x=element_text(hjust=0.5))
plotpositive
#ggsave('Positive-linear.png')
```

Linear regression
```{r}
emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
lm<-lm(DailyChange ~ angryf+anticipationf+disgustf+fearf+joyf+sadnessf+surprisef+trustf+negativef+positivef, data=emotionsfrequency)
summary(lm)
```

```{r}
llm<-lm(DailyChange ~ langryf+lanticipationf+ldisgustf+lfearf+ljoyf+lsadnessf+lsurprisef+ltrustf+lnegativef+lpositivef, data=emotionsfrequency)
summary(llm)
```

```{r}
emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
lmangry<-lm(DailyChange ~ angryf, data=emotionsfrequency)
summary(lmangry)
```

```{r}
emotionsfrequency$DailyChange<-as.numeric(emotionsfrequency$DailyChange)
llmangry<-lm(DailyChange ~ langryf, data=emotionsfrequency)
summary(llmangry)
```

```{r}
lmanticipation<-lm(DailyChange ~ anticipationf, data=emotionsfrequency)
summary(lmanticipation)
```

```{r}
llmanticipation<-lm(DailyChange ~ lanticipationf, data=emotionsfrequency)
summary(llmanticipation)
```

```{r}
lmdisgust<-lm(DailyChange ~ disgustf, data=emotionsfrequency)
summary(lmdisgust)
```

```{r}
llmdisgust<-lm(DailyChange ~ ldisgustf, data=emotionsfrequency)
summary(llmdisgust)
```

```{r}
lmfear<-lm(DailyChange ~ fearf, data=emotionsfrequency)
summary(lmfear)
```

```{r}
llmfear<-lm(DailyChange ~ lfearf, data=emotionsfrequency)
summary(llmfear)
```

```{r}
lmjoy<-lm(DailyChange ~ joyf, data=emotionsfrequency)
summary(lmjoy)
```

```{r}
llmjoy<-lm(DailyChange ~ ljoyf, data=emotionsfrequency)
summary(llmjoy)
```

```{r}
lmsadness<-lm(DailyChange ~ sadnessf, data=emotionsfrequency)
summary(lmsadness)
```

```{r}
llmsadness<-lm(DailyChange ~ lsadnessf, data=emotionsfrequency)
summary(llmsadness)
```

```{r}
lmsurprise<-lm(DailyChange ~ surprisef, data=emotionsfrequency)
summary(lmsurprise)
```

```{r}
llmsurprise<-lm(DailyChange ~ lsurprisef, data=emotionsfrequency)
summary(llmsurprise)
```

```{r}
lmtrust<-lm(DailyChange ~ trustf, data=emotionsfrequency)
summary(lmtrust)
```

```{r}
llmtrust<-lm(DailyChange ~ ltrustf, data=emotionsfrequency)
summary(llmtrust)
```

```{r}
lmnegative<-lm(DailyChange ~ negativef, data=emotionsfrequency)
summary(lmnegative)
```

```{r}
llmnegative<-lm(DailyChange ~ lnegativef, data=emotionsfrequency)
summary(llmnegative)
```

```{r}
lmpositive<-lm(DailyChange ~ positivef, data=emotionsfrequency)
summary(lmpositive)
```

```{r}
llmpositive<-lm(DailyChange ~ lpositivef, data=emotionsfrequency)
summary(llmpositive)
```

```{r}
library(stargazer)
stargazer(llm, type="text", title="Emotion Analysis: Multilinear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multilinear Regression Result (Log).txt")
stargazer(llm, type="text", title="Emotion Analysis: Multilinear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multilinear Regression Result (Log).pdf")
```

```{r}
library(stargazer)
stargazer(lm, type="text", title="Emotion Analysis: Multilinear Regression Result", digits=1, out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis:Multilinear Regression Result.txt")
```

```{r}
stargazer(llmangry, llmanticipation, llmdisgust, llmfear, llmjoy, llmsadness, llmsurprise, llmtrust, llmpositive, llmnegative,  type="text", title="Emotion Analysis:Linear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Linear Regression Result (Log).txt")
```

```{r}
stargazer(lmangry, lmanticipation, lmdisgust, lmfear, lmjoy, lmsadness, lmsurprise, lmtrust, lmpositive, lmnegative,  type="text", title="Emotion Analysis:Linear Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Linear Regression Result.txt")
```


Prepare corpus for identifying emotions.

For tweets that are associated with daily stock price increase/decrease:
```{r}
iTesla<-Tesla %>%
  filter(DailyChange>0)
dTesla<-Tesla %>%
  filter(DailyChange<0)
```

```{r prepare corpus for identifying emotions}
#Increasing daily stock prices
doc_id<-c(1:80)
line<-c(rep(1,80))
text<-data.frame(doc_id, text=c(iTesla$tweet),line, stringsAsFactors=FALSE)
i_df_source<-DataframeSource(text)
i_df_nrc<-VCorpus(i_df_source)
i_nrc_clean<-clean_corpus(i_df_nrc)
i_nrc_dtm<-DocumentTermMatrix(i_nrc_clean)
i_nrc_m<-as.matrix(i_nrc_dtm)
i_nrc_td<-tidy(i_nrc_dtm)
i_nrc_tf_idf<-i_nrc_td %>%
  bind_tf_idf(term, document, count)
i_nrc_tf_idf<-i_nrc_tf_idf %>%
  arrange(desc(tf_idf))
i_nrc_tf_idf
```

```{r prepare corpus 2nd for identifying emotions}
#Decreasing daily stock prices
doc_id<-c(1:46)
line<-c(rep(1,46))
text<-data.frame(doc_id, text=c(dTesla$tweet),line, stringsAsFactors=FALSE)
d_df_source<-DataframeSource(text)
d_df_nrc<-VCorpus(d_df_source)
d_nrc_clean<-clean_corpus(d_df_nrc)
d_nrc_dtm<-DocumentTermMatrix(d_nrc_clean)
d_nrc_m<-as.matrix(d_nrc_dtm)
d_nrc_td<-tidy(d_nrc_dtm)
d_nrc_tf_idf<-d_nrc_td %>%
  bind_tf_idf(term, document, count)
d_nrc_tf_idf<-d_nrc_tf_idf %>%
  arrange(desc(tf_idf))
d_nrc_tf_idf
```

### Angry

Find tf_idf score for angry words used in the tweets.
```{r anger, echo=TRUE, eval=TRUE}
ianger<-i_nrc_tf_idf %>%
  filter(term %in% nrc_anger$word) %>%
  select(term,tf_idf)
ianger<-rename(ianger, i_tf_idf=tf_idf)
danger<-d_nrc_tf_idf %>%
  filter(term %in% nrc_anger$word) %>%
  select(term,tf_idf)
danger<-rename(danger, d_tf_idf=tf_idf)
angerwords<-full_join(ianger, danger,by="term")
#Replace all NA values as 0
angerwords$i_tf_idf[is.na(angerwords$i_tf_idf)]<-0
angerwords$d_tf_idf[is.na(angerwords$d_tf_idf)]<-0
angerwords
```

Visualize the relationship between the use of words from the angry category and stock price change. 
```{r plot anger, echo=TRUE, eval=TRUE}
plot2<-angerwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(angry words) in tweets associated with stock price increase", y="f(angry words) in tweets associated with stock price decrease", title="Frequency of angry words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))#+
  #facet_zoom(x=i_tf_idf<=0.5, y=d_tf_idf<=0.5)
plot2
#ggsave('angry diff.png')
```

### Anticipation

Find tf_idf score for anticipation words used in the tweets.
```{r anticipation, echo=TRUE, eval=TRUE}
ianticipation<-i_nrc_tf_idf %>%
  filter(term %in% nrc_anticipation$word) %>%
  select(term,tf_idf)
ianticipation<-rename(ianticipation, i_tf_idf=tf_idf)
danticipation<-d_nrc_tf_idf %>%
  filter(term %in% nrc_anticipation$word) %>%
  select(term,tf_idf)
danticipation<-rename(danticipation, d_tf_idf=tf_idf)
anticipationwords<-full_join(ianticipation, danticipation,by="term")
#Replace all NA values as 0
anticipationwords$i_tf_idf[is.na(anticipationwords$i_tf_idf)]<-0
anticipationwords$d_tf_idf[is.na(anticipationwords$d_tf_idf)]<-0
anticipationwords
```

Visualize the relationship between the use of words from the anticipation category and stock price change. 
```{r plot anticipation, echo=TRUE, eval=TRUE}
plot3<-anticipationwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(anticipation words) in tweets associated with stock price increase", y="f(anticipation words) in tweets associated with stock price decrease", title="Frequency of anticipation words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot3
#ggsave('anticipation diff.png')
```

### Disgust

Find tf_idf score for disgust words used in the tweets.
```{r disgust, echo=TRUE, eval=TRUE}
idisgust<-i_nrc_tf_idf %>%
  filter(term %in% nrc_disgust$word) %>%
  select(term,tf_idf)
idisgust<-rename(idisgust, i_tf_idf=tf_idf)
ddisgust<-d_nrc_tf_idf %>%
  filter(term %in% nrc_disgust$word) %>%
  select(term,tf_idf)
ddisgust<-rename(ddisgust, d_tf_idf=tf_idf)
disgustwords<-full_join(idisgust, ddisgust,by="term")
#Replace all NA values as 0
disgustwords$i_tf_idf[is.na(disgustwords$i_tf_idf)]<-0
disgustwords$d_tf_idf[is.na(disgustwords$d_tf_idf)]<-0
disgustwords
```

Visualize the relationship between the use of words from the disgust category and stock price change 
```{r plot disgust, echo=TRUE, eval=TRUE}
plot4<-disgustwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(disgust words) in tweets associated with stock price increase", y="f(disgust words) in tweets associated with stock price decrease", title="Frequency of disgust words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot4
#ggsave('disgust diff.png')
```

### Fear

Find tf_idf score for fear words used in the tweets.
```{r fear, echo=TRUE, eval=TRUE}
ifear<-i_nrc_tf_idf %>%
  filter(term %in% nrc_fear$word) %>%
  select(term,tf_idf)
ifear<-rename(ifear, i_tf_idf=tf_idf)
dfear<-d_nrc_tf_idf %>%
  filter(term %in% nrc_fear$word) %>%
  select(term,tf_idf)
dfear<-rename(dfear, d_tf_idf=tf_idf)
fearwords<-full_join(ifear, dfear,by="term")
#Replace all NA values as 0
fearwords$i_tf_idf[is.na(fearwords$i_tf_idf)]<-0
fearwords$d_tf_idf[is.na(fearwords$d_tf_idf)]<-0
fearwords
```

Visualize the relationship between the use of words from the fear category and stock price change. 
```{r plot fear, echo=TRUE, eval=TRUE}
plot5<-fearwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(fear words) in tweets associated with stock price increase", y="f(fear words) in tweets associated with stock price decrease", title="Frequency of fear words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot5
#ggsave('fear diff.png')
```

### Joy

Find tf_idf score for joy words used in the tweets.
```{r joy, echo=TRUE, eval=TRUE}
ijoy<-i_nrc_tf_idf %>%
  filter(term %in% nrc_joy$word) %>%
  select(term,tf_idf)
ijoy<-rename(ijoy, i_tf_idf=tf_idf)
djoy<-d_nrc_tf_idf %>%
  filter(term %in% nrc_joy$word) %>%
  select(term,tf_idf)
djoy<-rename(djoy, d_tf_idf=tf_idf)
joywords<-full_join(ijoy, djoy,by="term")
#Replace all NA values as 0
joywords$i_tf_idf[is.na(joywords$i_tf_idf)]<-0
joywords$d_tf_idf[is.na(joywords$d_tf_idf)]<-0
joywords
```

Visualize the relationship between the use of words from the joy category and stock price change.
```{r plot joy, echo=TRUE, eval=TRUE}
plot6<-joywords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(joy words) in tweets associated with stock price increase", y="f(joy words) in tweets associated with stock price decrease", title="Frequency of joy words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot6
#ggsave('joy diff.png')
```

### Sadness

Find tf_idf score for sadness words used in the tweets.
```{r sadness, echo=TRUE, eval=TRUE}
isadness<-i_nrc_tf_idf %>%
  filter(term %in% nrc_sadness$word) %>%
  select(term,tf_idf)
isadness<-rename(isadness, i_tf_idf=tf_idf)
dsadness<-d_nrc_tf_idf %>%
  filter(term %in% nrc_sadness$word) %>%
  select(term,tf_idf)
dsadness<-rename(dsadness, d_tf_idf=tf_idf)
sadnesswords<-full_join(isadness, dsadness,by="term")
#Replace all NA values as 0
sadnesswords$i_tf_idf[is.na(sadnesswords$i_tf_idf)]<-0
sadnesswords$d_tf_idf[is.na(sadnesswords$d_tf_idf)]<-0
sadnesswords
```

Visualize the relationship between the use of words from the sadness category and stock price change.
```{r plot sadness, echo=TRUE, eval=TRUE}
plot7<-sadnesswords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(sadness words) in tweets associated with stock price increase", y="f(sadness words) in tweets associated with stock price decrease", title="Frequency of sadness words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2.5), breaks=seq(0,2.5,0.5))+
  scale_y_continuous(limits=c(0,2.5), breaks=seq(0,2.5,0.5))
plot7
#ggsave('sadness diff.png')
```
### Surprise

Find tf_idf score for surprise words used in the tweets.
```{r surprise, echo=TRUE, eval=TRUE}
isurprise<-i_nrc_tf_idf %>%
  filter(term %in% nrc_surprise$word) %>%
  select(term,tf_idf)
isurprise<-rename(isurprise, i_tf_idf=tf_idf)
dsurprise<-d_nrc_tf_idf %>%
  filter(term %in% nrc_surprise$word) %>%
  select(term,tf_idf)
dsurprise<-rename(dsurprise, d_tf_idf=tf_idf)
surprisewords<-full_join(isurprise, dsurprise,by="term")
#Replace all NA values as 0
surprisewords$i_tf_idf[is.na(surprisewords$i_tf_idf)]<-0
surprisewords$d_tf_idf[is.na(surprisewords$d_tf_idf)]<-0
surprisewords
```

Visualize the relationship between the use of words from the surprise category and stock price change.
```{r plot surprise, echo=TRUE, eval=TRUE}
plot8<-surprisewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(surprise words) in tweets associated with stock price increase", y="f(surprise words) in tweets associated with stock price decrease", title="Frequency of surprise words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot8
#ggsave('surprise diff.png')
```

### Trust

Find tf_idf score for trust words used in the tweets.
```{r trust, echo=TRUE, eval=TRUE}
itrust<-i_nrc_tf_idf %>%
  filter(term %in% nrc_trust$word) %>%
  select(term,tf_idf)
itrust<-rename(itrust, i_tf_idf=tf_idf)
dtrust<-d_nrc_tf_idf %>%
  filter(term %in% nrc_trust$word) %>%
  select(term,tf_idf)
dtrust<-rename(dtrust, d_tf_idf=tf_idf)
trustwords<-full_join(itrust, dtrust,by="term")
#Replace all NA values as 0
trustwords$i_tf_idf[is.na(trustwords$i_tf_idf)]<-0
trustwords$d_tf_idf[is.na(trustwords$d_tf_idf)]<-0
trustwords
```

Visualize the relationship between the use of words from the trust category and stock price change.
```{r plot trust, echo=TRUE, eval=TRUE}
plot9<-trustwords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(trust words) in tweets associated with stock price increase", y="f(trust words) in tweets associated with stock price decrease", title="Frequency of trust words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot9
#ggsave('trust diff.png')
```

### Negative

Find tf_idf score for negative words used in the tweets.
```{r negative, echo=TRUE, eval=TRUE}
inegative<-i_nrc_tf_idf %>%
  filter(term %in% nrc_negative$word) %>%
  select(term,tf_idf)
inegative<-rename(inegative, i_tf_idf=tf_idf)
dnegative<-d_nrc_tf_idf %>%
  filter(term %in% nrc_negative$word) %>%
  select(term,tf_idf)
dnegative<-rename(dnegative, d_tf_idf=tf_idf)
negativewords<-full_join(inegative, dnegative,by="term")
#Replace all NA values as 0
negativewords$i_tf_idf[is.na(negativewords$i_tf_idf)]<-0
negativewords$d_tf_idf[is.na(negativewords$d_tf_idf)]<-0
negativewords
```

Visualize the relationship between the use of words from the negative category and stock price change.
```{r plot negative, echo=TRUE, eval=TRUE}
plot10<-negativewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(negative words) in tweets associated with stock price increase", y="f(negative words) in tweets associated with stock price decrease", title="Frequency of negative words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,2), breaks=seq(0,2,0.5))+
  scale_y_continuous(limits=c(0,2), breaks=seq(0,2,0.5))
plot10
#ggsave('negative diff.png')
```

### Positive

Find tf_idf score for positive words used in the tweets.
```{r positive, echo=TRUE, eval=TRUE}
ipositive<-i_nrc_tf_idf %>%
  filter(term %in% nrc_positive$word) %>%
  select(term,tf_idf)
ipositive<-rename(ipositive, i_tf_idf=tf_idf)
dpositive<-d_nrc_tf_idf %>%
  filter(term %in% nrc_positive$word) %>%
  select(term,tf_idf)
dpositive<-rename(dpositive, d_tf_idf=tf_idf)
positivewords<-full_join(ipositive, dpositive,by="term")
#Replace all NA values as 0
positivewords$i_tf_idf[is.na(positivewords$i_tf_idf)]<-0
positivewords$d_tf_idf[is.na(positivewords$d_tf_idf)]<-0
positivewords
```

Visualize the relationship between the use of words from the positive category and stock price change.
```{r plot positive, echo=TRUE, eval=TRUE}
plot11<-positivewords %>%
  ggplot(aes(x=i_tf_idf,y=d_tf_idf))+
  geom_jitter(color="#00CED1")+
  theme_bw()+
  theme(legend.position="none")+
  labs(x="f(positive words) in tweets associated with stock price increase", y="f(positive words) in tweets associated with stock price decrease", title="Frequency of positive words in tweets associated with stock price increase/decrease")+
  theme(plot.title=element_text(hjust=0.5))+
  scale_x_continuous(limits=c(0,3), breaks=seq(0,3,0.5))+
  scale_y_continuous(limits=c(0,3), breaks=seq(0,3,0.5))
plot11
#ggsave('positive diff.png')
```

Logistic Regression

```{r}
emotionsfrequency$Direction[emotionsfrequency$DailyChange>0]<-1
emotionsfrequency$Direction[emotionsfrequency$DailyChange==0]<-0
emotionsfrequency$Direction[emotionsfrequency$DailyChange<0]<--1
emotionsfrequency$Direction<-as.factor(emotionsfrequency$Direction)
emotionsfrequency
```

```{r}
logit<-glm(Direction ~ angryf+anticipationf+disgustf+fearf+joyf+sadnessf+surprisef+trustf+negativef+positivef, emotionsfrequency, family=binomial(link ="logit"))
summary(logit)
```

```{r}
stargazer(logit, type="text", title="Emotion Analysis: Multi-logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multi-logistic Regression Result.txt")
```

```{r}
llogit<-glm(Direction ~ langryf+lanticipationf+ldisgustf+lfearf+ljoyf+lsadnessf+lsurprisef+ltrustf+lpositivef+lnegativef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogit)
```

```{r}
stargazer(llogit, type="text", title="Emotion Analysis: Multi-logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Multi-logistic Regression Result (Log).txt")
```

```{r}
logitangry<-glm(Direction ~ angryf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitangry)
```

```{r}
llogitangry<-glm(Direction ~ langryf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitangry)
```

```{r}
logitanticipation<-glm(Direction ~ anticipationf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitanticipation)
```

```{r}
llogitanticipation<-glm(Direction ~ lanticipationf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitanticipation)
```

```{r}
logitdisgust<-glm(Direction ~ disgustf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitdisgust)
```

```{r}
llogitdisgust<-glm(Direction ~ ldisgustf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitdisgust)
```

```{r}
logitfear<-glm(Direction ~ fearf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitfear)
```

```{r}
llogitfear<-glm(Direction ~ lfearf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitfear)
```

```{r}
logitjoy<-glm(Direction ~ joyf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitjoy)
```

```{r}
llogitjoy<-glm(Direction ~ ljoyf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitjoy)
```

```{r}
logitsadness<-glm(Direction ~ sadnessf, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsadness)
```

```{r}
llogitsadness<-glm(Direction ~ lsadnessf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsadness)
```

```{r}
logitsurprise<-glm(Direction ~ surprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsurprise)
```

```{r}
llogitsurprise<-glm(Direction ~ lsurprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsurprise)
```

```{r}
logitsurprise<-glm(Direction ~ surprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitsurprise)
```

```{r}
llogitsurprise<-glm(Direction ~ lsurprisef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitsurprise)
```

```{r}
logittrust<-glm(Direction ~ trustf, emotionsfrequency, family=binomial(link ="logit"))
summary(logittrust)
```

```{r}
llogittrust<-glm(Direction ~ ltrustf, emotionsfrequency, family=binomial(link ="logit"))
summary(llogittrust)
```

```{r}
logitnegative<-glm(Direction ~ negativef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitnegative)
```

```{r}
llogitnegative<-glm(Direction ~ lnegativef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitnegative)
```

```{r}
logitpositive<-glm(Direction ~ positivef, emotionsfrequency, family=binomial(link ="logit"))
summary(logitpositive)
```

```{r}
llogitpositive<-glm(Direction ~ lpositivef, emotionsfrequency, family=binomial(link ="logit"))
summary(llogitpositive)
```

```{r}
stargazer(logitangry, logitanticipation, logitdisgust, logitfear, logitjoy, logitsadness, logitsurprise, logittrust, logitpositive, logitnegative,  type="text", title="Emotion Analysis: Logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Logistic Regression Result.txt")
```

```{r}
stargazer(llogitangry, llogitanticipation, llogitdisgust, llogitfear, llogitjoy, llogitsadness, llogitsurprise, llogittrust, llogitpositive, llogitnegative,  type="text", title="Emotion Analysis: Logistic Regression Result", out="/Users/annie/Desktop/Columbia Fall2021/Columbia Course Fall 2021/GR5067 NLP/Group Project/Tesla/Emotion Analysis: Logistic Regression Result (Log).txt")
```